/*
 Navicat Premium Data Transfer

 Source Server         : 测试环境
 Source Server Type    : MySQL
 Source Server Version : 50722
 Source Host           : 118.178.126.76
 Source Database       : aimix

 Target Server Type    : MySQL
 Target Server Version : 50722
 File Encoding         : utf-8

 Date: 11/27/2018 17:02:08 PM
*/

SET NAMES utf8mb4;
SET FOREIGN_KEY_CHECKS = 0;

-- ----------------------------
--  Table structure for `bas_config`
-- ----------------------------
DROP TABLE IF EXISTS `bas_config`;
CREATE TABLE `bas_config` (
  `id` bigint(19) unsigned NOT NULL AUTO_INCREMENT COMMENT '配置项ID',
  `name` varchar(128) DEFAULT NULL COMMENT '配置项名',
  `code` varchar(128) DEFAULT NULL COMMENT '配置项编码',
  `value` text COMMENT '配置项值',
  `ctime` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
  `mtime` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '修改时间',
  `invalid` tinyint(2) NOT NULL DEFAULT '0' COMMENT '0:有效; 1:无效',
  PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=2 DEFAULT CHARSET=utf8 COMMENT='组件配置项表';

-- ----------------------------
--  Records of `bas_config`
-- ----------------------------
BEGIN;
INSERT INTO `bas_config` VALUES ('1', '类型转换可选的类型', 'optionalType', 'bigint,double,string', '2018-09-11 11:54:52', '2018-09-11 11:54:52', '0');
COMMIT;

-- ----------------------------
--  Table structure for `bas_node`
-- ----------------------------
DROP TABLE IF EXISTS `bas_node`;
CREATE TABLE `bas_node` (
  `node_id` bigint(19) unsigned NOT NULL AUTO_INCREMENT COMMENT '组件ID',
  `type` tinyint(4) NOT NULL COMMENT '组件类型',
  `name` varchar(128) NOT NULL COMMENT '组件名称',
  `code` varchar(128) DEFAULT NULL COMMENT '组件编码',
  `descr` varchar(1024) DEFAULT NULL COMMENT '组件描述',
  `node_catalog_id` bigint(19) unsigned NOT NULL COMMENT '组件类目ID',
  `status` tinyint(4) NOT NULL DEFAULT '1' COMMENT '组件状态(1:草稿；2:上架可用；3:预下架；4:废弃)',
  `icon` varchar(256) DEFAULT NULL COMMENT '展示图标',
  `index` tinyint(4) NOT NULL DEFAULT '0' COMMENT '页面展示顺序',
  `owner_id` bigint(19) unsigned NOT NULL DEFAULT '0' COMMENT '责任人',
  `c_user_id` bigint(19) unsigned NOT NULL DEFAULT '0' COMMENT '创建人',
  `m_user_id` bigint(19) unsigned NOT NULL DEFAULT '0' COMMENT '修改人',
  `ctime` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
  `mtime` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '修改时间',
  `invalid` tinyint(2) NOT NULL DEFAULT '0' COMMENT '0:有效; 1:无效',
  `select_filed_type` tinyint(4) NOT NULL DEFAULT '2' COMMENT '组件选择字段方式:(1:表组件字段来源方；2:透传字段；3:选择字段；4:字段映射；5:字段类型修改;6:新增字段组件)',
  `predict_type` tinyint(4) NOT NULL DEFAULT '1' COMMENT '预测类型(1:保留；2:删除；3:转换为transform；4:模型组件)',
  PRIMARY KEY (`node_id`)
) ENGINE=InnoDB AUTO_INCREMENT=1004 DEFAULT CHARSET=utf8 COMMENT='公共组件表';

-- ----------------------------
--  Records of `bas_node`
-- ----------------------------
BEGIN;
INSERT INTO `bas_node` VALUES ('1', '1', '数据表', 'odps_source', '* 若表底层schema有更新，可以点击\"字段信息“中的刷新按钮来更新。* 读取数据的表数据组件，默认读取本工程下的数据；若读取其他工程的表数据且拥有该project的操作权限, 只需在表名前添加工程名，格式：工程名.表名，如：tianchi_project.weibo_data 当输入表后，会自动读取表的结构数据；', '1', '2', '1', '1', '0', '0', '0', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '1', '1', '1'), ('3', '8', '自定义SQL', 'Sql', '', '8', '2', '8', '1', '0', '0', '0', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0', '2', '1'), ('4', '8', '表转文件', 'table2file', '表转文件组件', '8', '2', '8', '2', '0', '0', '0', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0', '2', '1'), ('6', '7', '去停用词', 'stop_words_filter', '', '7', '2', '7', '1', '0', '0', '0', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0', '2', '1'), ('7', '7', '句子向量索引', 'vector_index', '', '7', '2', '7', '1', '0', '0', '0', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0', '2', '1'), ('9', '11', 'TextCnn', 'textcnn', '', '7', '2', '7', '1', '0', '0', '0', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '1', '2', '4'), ('10', '2', '随机采样', 'random_sample', '', '2', '2', '2', '1', '0', '0', '0', '2018-08-29 19:41:52', '2018-08-29 19:41:52', '0', '2', '2'), ('11', '2', '加权采样', 'weight_sample', '', '2', '2', '2', '2', '0', '0', '0', '2018-08-29 19:41:52', '2018-08-29 19:41:52', '0', '2', '2'), ('12', '2', '分层采样', 'stratified_sample', '', '2', '2', '2', '3', '0', '0', '0', '2018-08-29 19:41:52', '2018-08-29 19:41:52', '0', '2', '2'), ('13', '2', '过滤与映射', 'filter', '', '2', '2', '2', '4', '0', '0', '0', '2018-08-29 19:41:52', '2018-08-29 19:41:52', '0', '4', '1'), ('14', '2', '拆分', 'split_table', '', '2', '2', '2', '7', '0', '0', '0', '2018-08-29 19:41:52', '2018-08-29 19:41:52', '0', '2', '1'), ('15', '2', 'join', 'join', '', '2', '2', '2', '5', '0', '0', '0', '2018-08-29 19:41:52', '2018-08-29 19:41:52', '0', '3', '1'), ('16', '2', '增加序列号', 'add_id', '', '2', '2', '2', '3', '0', '0', '0', '2018-08-29 19:41:52', '2018-08-29 19:41:52', '0', '6', '4'), ('17', '2', '缺失值填充', 'missed_fill', '', '2', '2', '2', '8', '0', '0', '0', '2018-08-29 19:41:52', '2018-08-29 19:41:52', '0', '2', '3'), ('18', '2', '归一化', 'normalize', '', '2', '2', '2', '9', '0', '0', '0', '2018-08-29 19:41:52', '2018-08-29 19:41:52', '0', '5', '1'), ('19', '2', '标准化', 'standardize', '', '2', '2', '2', '10', '0', '0', '0', '2018-08-29 19:41:52', '2018-08-29 19:41:52', '0', '5', '1'), ('20', '2', '类型转换', 'type_transform', '', '2', '2', '2', '11', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '5', '1'), ('80', '8', '读模型', 'universal_model', '', '1', '2', '8', '3', '0', '0', '0', '2018-11-12 17:21:48', '2018-11-12 17:21:48', '0', '2', '1'), ('81', '8', '文件转表', 'file2table', '文件转表组件', '8', '2', '8', '3', '0', '0', '0', '2018-11-22 22:16:17', '2018-11-22 22:16:17', '0', '2', '1'), ('101', '1', '写数据表', 'write_table', '', '1', '2', '1', '1', '0', '0', '0', '2018-11-09 11:33:15', '2018-11-09 11:33:15', '0', '2', '1'), ('102', '1', '读数据表', 'read_table', '', '1', '2', '1', '1', '0', '0', '0', '2018-11-09 11:31:05', '2018-11-09 11:31:05', '0', '1', '1'), ('103', '1', '读文件', 'file_source', '文件组件，支持选择结构化、半结构化、非结构化文件', '1', '2', '1', '1', '0', '0', '0', '2018-11-17 17:02:33', '2018-11-17 17:02:33', '0', '2', '1'), ('201', '3', '主成分分析', 'princompanalysis', '', '3', '2', '3', '1', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '1'), ('202', '3', '特征尺度变换', 'scaletransform', '', '3', '2', '3', '2', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '1'), ('203', '3', '特征离散', 'discret', '', '3', '2', '3', '3', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '1'), ('204', '3', '特征异常平滑', 'abnormalsmooth', '', '3', '2', '3', '4', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '1'), ('205', '3', '随机森林特征重要性', 'randomforestfeatureimportance', '', '3', '2', '3', '5', '0', '0', '0', '2018-11-06 10:55:05', '2018-11-06 10:55:05', '0', '2', '1'), ('206', '3', '线性模型特征重要性', 'regression_feature_importance', '', '3', '2', '3', '6', '0', '0', '0', '2018-11-05 20:31:59', '2018-11-05 20:31:59', '0', '2', '1'), ('207', '3', '奇异值分解', 'svd', '', '3', '2', '3', '6', '0', '0', '0', '2018-11-06 18:04:18', '2018-11-06 18:04:18', '0', '2', '1'), ('208', '3', 'GBDT特征重要性', 'gbdtfeatureimportance', '', '3', '2', '3', '7', '0', '0', '0', '2018-11-07 13:22:16', '2018-11-07 13:22:16', '0', '2', '1'), ('209', '3', 'one-hot编码 ', 'onehot', '', '3', '2', '3', '8', '0', '0', '0', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '2', '1'), ('502', '11', '朴素贝叶斯分类', 'naivebayes', '', '5', '2', '5', '2', '0', '0', '0', '2018-11-08 20:17:31', '2018-11-08 20:17:31', '0', '2', '1'), ('506', '11', '线性回归', 'linearregression', '', '5', '2', '5', '6', '0', '0', '0', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0', '2', '1'), ('508', '10', '聚类模型评估', 'clusterevaluate', '', '5', '2', '9', '8', '0', '0', '0', '2018-11-08 20:24:12', '2018-11-08 20:24:12', '0', '2', '1'), ('511', '11', 'kmeans', 'kmeans', '', '5', '2', '5', '11', '0', '0', '0', '2018-11-13 17:12:47', '2018-11-13 17:12:47', '0', '2', '1'), ('515', '10', '二分类评估', 'binaryclassificationevaluate', '', '5', '2', '9', '15', '0', '0', '0', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '1', '2', '1'), ('599', '5', '机器学习预测', 'prediction', '机器学习预测组件', '5', '2', '5', '99', '0', '0', '0', '2018-11-10 14:52:14', '2018-11-10 14:52:14', '0', '2', '1'), ('600', '11', 'Tensorflow', 'tensorflow', '深度学习tensorflow框架', '6', '2', '6', '1', '0', '0', '0', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0', '2', '4'), ('601', '11', 'MXNet', 'mxnet', '深度学习MXNet框架', '6', '2', '6', '2', '0', '0', '0', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0', '2', '4'), ('602', '11', 'Caffe2', 'caffes', '深度学习Caffe2框架', '6', '2', '6', '3', '0', '0', '0', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0', '2', '4'), ('603', '11', 'xgboost', 'xgboost', '深度学习xgboost框架', '6', '2', '6', '4', '0', '0', '0', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0', '2', '4'), ('604', '11', 'LightGBM', 'lightgbm', '深度学习LightGBM框架', '6', '2', '6', '5', '0', '0', '0', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0', '2', '4'), ('605', '6', '深度学习预测', 'deeplearning', '深度学习通用框架', '6', '2', '6', '6', '0', '0', '0', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '2', '1'), ('700', '7', '词频统计', 'doc_word_stat', '', '7', '2', '7', '1', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '4'), ('701', '7', 'word2vec', 'word2vec', '', '7', '2', '7', '2', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '4'), ('702', '7', 'TF-IDF', 'tfidf', '', '7', '2', '7', '3', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '4'), ('703', '7', 'PLDA', 'plda', '', '7', '2', '7', '4', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '4'), ('704', '7', '文章相似度', 'doc_similarity', '', '7', '2', '7', '5', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '4'), ('705', '7', 'PMI', 'pmi', '', '7', '2', '7', '6', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '4'), ('706', '7', 'Doc2Vec', 'doc2vec', '', '7', '2', '7', '7', '0', '0', '0', '2018-09-11 17:13:19', '2018-09-11 17:13:19', '0', '2', '4'), ('707', '7', 'ngram_count', 'ngram_count', '', '7', '2', '7', '8', '0', '0', '0', '2018-11-12 19:12:04', '2018-11-12 19:12:04', '0', '2', '1'), ('708', '7', '分词处理', 'segmentpro', '', '7', '2', '7', '9', '0', '0', '0', '2018-11-02 21:28:16', '2018-11-02 21:28:16', '0', '2', '1'), ('709', '7', '文本摘要', 'abstract_generate', '', '7', '2', '7', '10', '0', '0', '0', '2018-11-05 16:31:48', '2018-11-05 16:31:48', '0', '2', '1'), ('710', '7', '语义向量距离', 'semantic_vector_distance', '', '7', '2', '7', '11', '0', '0', '0', '2018-11-05 17:59:34', '2018-11-05 17:59:34', '0', '2', '1'), ('711', '7', '关键词抽取', 'keywords_extraction', '', '7', '2', '7', '11', '0', '0', '0', '2018-11-08 20:43:52', '2018-11-08 20:43:52', '0', '2', '1'), ('713', '7', '字符串相似度', 'string_similarity', '', '7', '2', '7', '7', '0', '0', '0', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '2', '1'), ('714', '7', '句子拆分', 'split_sentences', '', '7', '2', '7', '8', '0', '0', '0', '2018-11-12 17:15:00', '2018-11-12 17:15:00', '0', '2', '1'), ('715', '7', '三元组转kv', 'triple2kv', '', '7', '2', '7', '15', '0', '0', '0', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '2', '1'), ('800', '5', 'k近邻', 'knn', '', '5', '2', '5', '1', '0', '0', '0', '2018-11-05 19:22:21', '2018-11-05 19:22:21', '0', '2', '1'), ('803', '11', '决策树分类', 'decisiontree', '', '5', '2', '5', '1', '0', '0', '0', '2018-11-08 15:48:13', '2018-11-08 15:48:13', '0', '2', '1'), ('804', '11', '随机森林回归', 'randomforestregression', '', '5', '2', '5', '3', '0', '0', '0', '2018-11-05 21:12:13', '2018-11-05 21:12:13', '0', '2', '1'), ('805', '11', 'GBDT回归', 'gbtregression', '', '5', '2', '5', '1', '0', '0', '0', '2018-11-08 15:44:22', '2018-11-08 15:44:22', '0', '2', '1'), ('808', '11', '随机森林分类', 'randomforest', '', '5', '2', '5', '10', '0', '0', '0', '2018-11-16 19:37:48', '2018-11-16 19:37:48', '0', '2', '1'), ('809', '11', 'GBDT二分类', 'gbdt', '', '5', '2', '5', '11', '0', '0', '0', '2018-11-16 20:04:31', '2018-11-16 20:04:31', '0', '2', '1'), ('810', '11', '高斯混合聚类', 'gaussianmixture', '', '5', '2', '5', '11', '0', '0', '0', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '2', '1'), ('815', '11', '逻辑回归分类', 'logisticregression', '', '5', '2', '5', '15', '0', '0', '0', '2018-11-16 20:34:47', '2018-11-16 20:34:47', '0', '2', '1'), ('816', '11', '线性支持向量机', 'linearsvc', '', '5', '2', '5', '16', '0', '0', '0', '2018-11-16 21:03:58', '2018-11-16 21:03:58', '0', '2', '1'), ('1002', '9', '预测输入', 'predict_input', '仅预测实验可用，表示预测服务数据输入节点', '1', '2', '1', '2', '0', '0', '0', '2018-10-15 09:52:48', '2018-10-15 09:52:48', '1', '1', '1'), ('1003', '9', '预测输出', 'predict_output', '仅预测实验可以，表示预测服务数据输出节点', '1', '2', '1', '3', '0', '0', '0', '2018-10-15 09:54:44', '2018-10-15 09:54:44', '1', '1', '1');
COMMIT;

-- ----------------------------
--  Table structure for `bas_node_catalog`
-- ----------------------------
DROP TABLE IF EXISTS `bas_node_catalog`;
CREATE TABLE `bas_node_catalog` (
  `node_catalog_id` bigint(19) unsigned NOT NULL AUTO_INCREMENT COMMENT ' 组件类目id',
  `parent_id` bigint(19) unsigned NOT NULL DEFAULT '0' COMMENT '父目录ID,根目录时取0值',
  `name` varchar(64) NOT NULL DEFAULT '' COMMENT '目录名称',
  `category` varchar(32) NOT NULL DEFAULT '' COMMENT '组件类目-类别',
  `icon` varchar(256) DEFAULT NULL COMMENT '展示图标',
  `index` tinyint(6) NOT NULL DEFAULT '0' COMMENT '页面展示顺序',
  `mtime` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '修改时间',
  `ctime` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
  `invalid` tinyint(2) NOT NULL DEFAULT '0' COMMENT '0:有效; 1:无效',
  PRIMARY KEY (`node_catalog_id`)
) ENGINE=InnoDB AUTO_INCREMENT=9 DEFAULT CHARSET=utf8 COMMENT='组件目录表';

-- ----------------------------
--  Records of `bas_node_catalog`
-- ----------------------------
BEGIN;
INSERT INTO `bas_node_catalog` VALUES ('1', '0', '源 / 目标', 'source', '', '1', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0'), ('2', '0', '数据预处理', 'preprocess', '', '2', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0'), ('3', '0', '特征工程', 'featureEngineer', '', '3', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0'), ('4', '0', '统计分析', 'analytics', '', '4', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0'), ('5', '0', '机器学习', 'training', '', '5', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0'), ('6', '0', '深度学习', 'deepLearning', '', '6', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0'), ('7', '0', '文本分析', 'training', '', '7', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0'), ('8', '0', '工具', 'user_code', '', '8', '2018-08-21 10:27:39', '2018-08-21 10:27:39', '0');
COMMIT;

-- ----------------------------
--  Table structure for `bas_node_config`
-- ----------------------------
DROP TABLE IF EXISTS `bas_node_config`;
CREATE TABLE `bas_node_config` (
  `config_id` bigint(19) unsigned NOT NULL AUTO_INCREMENT COMMENT '配置项ID',
  `node_id` bigint(19) unsigned NOT NULL COMMENT '组件ID',
  `tab_id` bigint(19) unsigned NOT NULL COMMENT '配置tab页ID',
  `tab_index` tinyint(6) NOT NULL DEFAULT '1' COMMENT '配置tab页展示顺序',
  `name` varchar(128) DEFAULT NULL COMMENT '配置项名',
  `code` varchar(128) DEFAULT NULL COMMENT '配置项编码',
  `type` varchar(128) DEFAULT NULL COMMENT '配置项类型：number、String、labelColSelect',
  `value` text COMMENT '配置项默认值',
  `tip` varchar(128) DEFAULT NULL COMMENT '配置项提示：可选、必须、至少选择一项',
  `descr` varchar(512) DEFAULT NULL COMMENT '配置项长描述',
  `index` tinyint(6) NOT NULL DEFAULT '0' COMMENT '配置项展示顺序',
  `param` varchar(2048) DEFAULT NULL COMMENT '配置项扩展字段',
  `ctime` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
  `mtime` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '修改时间',
  `invalid` tinyint(2) NOT NULL DEFAULT '0' COMMENT '0:有效; 1:无效',
  `display` tinyint(4) DEFAULT '3' COMMENT 'display:1-仅页面展示不是组件参数；2-页面不展示但是是组件参数；3-页面展示并且是组件参数;4-页面不展示并且不是组件参数',
  `required` tinyint(4) DEFAULT '0' COMMENT '是否必填(0:非必填;1:必填)',
  `parsing_rule` tinyint(4) DEFAULT '1' COMMENT '配置项值解析规则(1:默认不解析；2:表字段_类型转换解析；3:表字段_字段修改解析；4:表字段解析)',
  `model_op_rule` tinyint(4) NOT NULL DEFAULT '1' COMMENT '模型处理规则(1. 不做处理 2. transform组件中间结果配置项 3. 模型组件模型配置文件)',
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) ENGINE=InnoDB AUTO_INCREMENT=993 DEFAULT CHARSET=utf8 COMMENT='组件配置项表';

-- ----------------------------
--  Records of `bas_node_config`
-- ----------------------------
BEGIN;
INSERT INTO `bas_node_config` VALUES ('1', '1', '1', '1', '表名', 'output_table', 'dataPreview', '', '表名', '', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '1', '1', '1'), ('7', '3', '3', '1', '输入源', 'input_table', 'sourceInput', '', '', '', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '1', '1', '1', '1'), ('8', '3', '3', '1', 'SQL脚本', 'sql', 'sqlEditor', '', '', '', '2', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '1', '1', '1'), ('9', '3', '3', '1', '结果表', 'output_table', 'input', '', '', '', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '2', '1', '1', '1'), ('10', '4', '4', '1', '文件目录', 'file_path', 'filePath', '', '', '', '1', '{“directoryOnly”:true}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '1', '1', '1'), ('11', '4', '4', '1', '表过滤sql', 'where', 'input', '', '', '', '2', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('12', '4', '4', '1', '文件名', 'file_name', 'input', '', '', '', '3', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('13', '4', '4', '1', '文件数量', 'file_num', 'intInput', '1', '', '转成文件个数', '4', '{\"type\":\"int\"}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('14', '4', '4', '1', '分隔符', 'field_delim', 'input', '\\t', '', 'text格式文件分隔符', '5', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('15', '4', '4', '1', '忽略列名', 'skip_header', 'boolean', 'true', '', '', '6', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('16', '4', '4', '1', '文件格式', 'file_type', 'select', 'text', '', '', '7', '{\"options\":[{\"value\":\"text\",\"key\":\"text\"}, {\"value\":\"csv\",\"key\":\"csv\"}, {\"value\":\"orc\",\"key\":\"orc\"}, {\"value\":\"parquet\",\"key\":\"parquet\"}]}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('17', '4', '4', '1', '输入表', 'table', 'input', '', '', '', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '2', '1', '1', '1'), ('24', '6', '6', '1', '选择停用词列', 'selected_cols', 'selectField', '', '', '多个以,分割', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '1', '4', '1'), ('25', '6', '6', '1', '配置停用词表', 'stopwords_table', 'dataPreview', '', '', '', '3', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '1', '1', '2'), ('26', '6', '6', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '2', '1', '1', '1'), ('27', '6', '6', '1', '结果表', 'output_table', 'input', '', '', '', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '2', '1', '1', '1'), ('28', '7', '7', '1', '索引化列', 'selected_cols', 'selectField', '', '', '多个以,分割', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '1', '4', '1'), ('29', '7', '7', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '2', '1', '1', '1'), ('30', '7', '7', '1', '结果表', 'output_table', 'input', '', '', '', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '2', '1', '1', '1'), ('31', '9', '9', '1', '句子长度(64,200or400)', 'sequence_length', 'intInput', '', '', '', '1', '{\"type\":\"int\", \"min\":0}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '1', '1', '1'), ('32', '9', '9', '1', '嵌入层长度(128or200)', 'embedding_size', 'intInput', '', '', '', '1', '{\"type\":\"int\", \"min\":0}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '1', '1', '1'), ('33', '9', '9', '1', '卷积层过滤长度(字符串\"2,3,4,5or2\")', 'filter_sizes', 'input', '', '', '', '1', '{\"type\":\"string\"}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '1', '1', '1'), ('34', '9', '9', '1', '卷积层高度(例100)', 'num_filters', 'intInput', '', '', '', '1', '{\"type\":\"int\", \"min\":0}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '1', '1', '1'), ('36', '9', '9', '1', '迭代训练次数', 'epoch', 'intInput', '50', '', '', '1', '{\"type\":\"int\", \"min\":0}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('37', '9', '9', '1', '批次长度', 'batch', 'intInput', '50', '', '', '1', '{\"type\":\"int\", \"min\":0}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('38', '9', '9', '1', 'drop_out', 'drop_out', 'floatInput', '0.5', '', '', '1', '{\"type\":\"float\", \"max\":1, \"min\":0}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('39', '9', '9', '1', '验证集比例', 'test_size', 'floatInput', '0.1', '', '', '1', '{\"type\":\"float\", \"max\":1, \"min\":0}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('40', '9', '9', '1', '学习率', 'learn_rate', 'floatInput', '0.001', '', '', '1', '{\"type\":\"float\", \"max\":1, \"min\":0}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '3', '0', '1', '1'), ('41', '9', '9', '1', '输入路径', 'input_path', 'input', '', '', '', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '2', '1', '1', '1'), ('42', '9', '9', '1', '结果路径', 'output_path', 'input', '', '', '', '1', '{}', '2018-08-24 18:57:16', '2018-08-24 18:57:16', '0', '2', '1', '1', '3'), ('56', '10', '10', '1', '采样类型', 'sample_type', 'select', '_size', '进行随机采样的类型，包括采样个数、采样比例两种，采样个数指按个数进行采样；采样比例，指按比例进行采样', '', '3', '{\"options\":[{\"key\":\"采样个数\",\"value\":\"_size\"},{\"key\":\"采样比例\",\"value\":\"_ratio\"}]}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('57', '10', '10', '1', '采样个数', 'sample_size', 'intInput', '', '采样数据的个数', '', '4', '{\"type\":\"int\",\"min\":\"1\"}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('58', '10', '10', '1', '采样比例', 'sample_ratio', 'floatInput', '', '采样的数据占总数的比例', '', '5', '{\"type\":\"float\",\"max\":\"1.00\"}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('59', '10', '10', '1', '放回采样', 'replace', 'boolean', 'false', '是否采用放回采样', '', '6', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('60', '10', '10', '1', '随机数种子', 'random_seed', 'intInput', '', '若希望每次生成的结果表相同，可以配置一个随机数种子', '', '7', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '1', '1'), ('61', '10', '10', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('62', '10', '10', '1', '结果表', 'output_table', 'input', '', '', '', '2', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('63', '11', '11', '1', '采样类型', 'sample_type', 'select', '_size', '采样的数据占总数的比例', '', '3', '{\"options\":[{\"key\":\"采样个数\",\"value\":\"_size\"},{\"key\":\"采样比例\",\"value\":\"_ratio\"}]}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('64', '11', '11', '1', '采样个数', 'sample_size', 'intInput', '', '采样数据的个数', '', '4', '{\"type\":\"int\",\"min\":\"1\"}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('65', '11', '11', '1', '采样比例', 'sample_ratio', 'floatInput', '', '采样的数据占总数的比例', '', '5', '{\"type\":\"float\",\"max\":\"1.00\"}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('66', '11', '11', '1', '放回采样', 'replace', 'boolean', 'false', '是否采用放回采样', '', '6', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('67', '11', '11', '1', '加权列', 'prob_col', 'selectField', '', '每个值代表该行数据所在全部数据中出现的权重，数值越大被采样的概率越高', '', '7', '{\"limitSize\":1}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '4', '1'), ('68', '11', '11', '1', '随机数种子', 'random_seed', 'intInput', '', '若希望每次生成的结果表相同，可以配置一个随机数种子', '', '8', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '1', '1'), ('69', '11', '11', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('70', '11', '11', '1', '结果表', 'output_table', 'input', '', '', '', '2', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('71', '12', '12', '1', '分组列', 'strata_col_name', 'selectField', '', '分层采样依据此列分层	', '', '3', '{\"limitSize\":1}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '4', '1'), ('72', '12', '12', '1', '采样类型', 'sample_type', 'select', '_size', '进行分层采样的类型，包括采样个数、采样比例两种，采样个数指按个数进行采样；采样比例，指按比例进行采样', '', '4', '{\"options\":[{\"key\":\"采样个数\",\"value\":\"_size\"},{\"key\":\"采样比例\",\"value\":\"_ratio\"}]}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('73', '12', '12', '1', '采样个数', 'sample_size', 'input', '', '采样数据的个数	', '', '5', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('74', '12', '12', '1', '采样比例', 'sample_ratio', 'input', '', '采样的数据占总数的比例	', '', '6', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('75', '12', '12', '1', '随机数种子', 'random_seed', 'intInput', '', '若希望每次生成的结果表相同，可以配置一个随机数种子', '', '7', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '1', '1'), ('76', '12', '12', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('77', '12', '12', '1', '结果表', 'output_table', 'input', '', '', '', '2', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('78', '13', '13', '1', '映射规则', 'column_transform', 'selectField', '', '', '', '4', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '3', '1'), ('79', '13', '13', '1', '过滤条件', 'filter', 'textarea', '', '输入where后的过滤表达式', '', '3', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('80', '13', '13', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('81', '13', '13', '1', '结果表', 'output_table', 'input', '', '', '', '2', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('82', '14', '14', '1', '拆分方式', 'split_type', 'select', '_ratio', '支持：按比例拆分、按阈值拆分', '', '4', '{\"options\":[{\"key\":\"按比例拆分\",\"value\":\"_ratio\"},{\"key\":\"按阈值拆分\",\"value\":\"_threshold\"}]}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '1', '0', '1', '1'), ('83', '14', '14', '1', '切分比例', 'proportion', 'floatInput', '0.8', '输出桩1的数据占全部数据的比例', '', '5', '{\"type\":\"float\",\"min\":0,\"max\":1}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '1', '1'), ('84', '14', '14', '1', '阈值列', 'threshold_column', 'selectField', '', '不支持string列', '', '6', '{\"limitSize\":1}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '4', '1'), ('86', '14', '14', '1', '阈值', 'threshold_val', 'input', '', '输出桩1的阈值列数值小于等于阈值，输出桩2的数值大于阈值	', '', '7', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '1', '1'), ('87', '14', '14', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('88', '14', '14', '1', '结果表1', 'output1_table', 'input', '', '', '', '2', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('89', '14', '14', '1', '结果表2', 'output2_table', 'input', '', '', '', '3', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('90', '15', '15', '1', '连接类型', 'join_type', 'select', 'left', '连接类型支持：左连接、右连接、内连接、全连接', '', '4', '{\"options\":[{\"key\":\"左连接\",\"value\":\"left\"},{\"key\":\"右连接\",\"value\":\"right\"},{\"key\":\"内连接\",\"value\":\"inner\"},{\"key\":\"全连接\",\"value\":\"full\"}]}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('91', '15', '15', '1', '关联条件', 'where', 'joinSelect', '', '两张表join的关联条件，目前只支持等式。', '', '5', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('92', '15', '15', '1', '选择左表字段', 'selected_left_cols', 'selectField', '', '选择左表字段', '', '6', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '4', '1'), ('93', '15', '15', '1', '选择右表字段', 'selected_right_cols', 'selectField', '', '选择右表字段', '', '7', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '4', '1'), ('94', '15', '15', '1', '连接左表', 'input_left_table', 'input', '', '连接左表', '', '1', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('95', '15', '15', '1', '连接右表', 'input_right_table', 'input', '', '连接右表', '', '2', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('96', '15', '15', '1', '结果表', 'output_table', 'input', '', '输出表', '', '3', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('97', '16', '16', '1', '序号列的名称', 'add_id_name', 'input', '', '添加的序号列的名称，从0开始编号，依次加1', '', '3', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '1', '1'), ('98', '16', '16', '1', '输入表', 'input_table', 'input', '', '	需要增加序列号的数据表', '', '1', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('99', '16', '16', '1', '结果表', 'output_table', 'input', '', '在原表基础上添加过序列号后的结果表', '', '2', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('100', '17', '17', '1', '选择字段', 'selected_cols', 'selectField', '', '默认全选字段', '', '3', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '4', '1'), ('101', '17', '17', '1', '原值', 'old_val', 'select', 'null', '原始的缺失值，可选Null、空字符、Null和空字符以及自定义的值', '', '4', ' {\"options\":[{\"key\":\"Null\",\"value\":\"null\"},{\"key\":\"空字符\",\"value\":\"empty\"},{\"key\":\"Null 和 空字符\",\"value\":\"null-empty\"},{\"key\":\"自定义(string)\",\"value\":\"user-defined\"}]}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '1', '1'), ('102', '17', '17', '1', '替换为', 'new_val', 'select', 'min()', '填充值，可选最小值、最大值、平均值以及自定义的值', '', '5', '{\"options\":[{\"key\":\"Min(数值型)\",\"value\":\"min()\"},{\"key\":\"Max(数值型)\",\"value\":\"max()\"},{\"key\":\"平均值(数值型)\",\"value\":\"avg()\"},{\"key\":\"自定义(数值型和string)\",\"value\":\"user-defined\"}]}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '1', '1'), ('103', '17', '17', '1', '高级选项', 'configs', 'input', '', '配置参数，要求格式–列名:原始值:替换值。其中原始值支持null,empty,null-empty和特定值；\"替换值支持max(),min(),avg()等聚合函数，多个配置以,分割或list传入', '', '6', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '1', '1'), ('104', '17', '17', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('105', '17', '17', '1', '结果表', 'output_table', 'input', '', '', '', '2', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('106', '18', '18', '1', '选择字段', 'selected_cols', 'selectField', '', '默认全选字段', '', '3', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '4', '1'), ('107', '18', '18', '1', '保留原始列', 'keep_original', 'boolean', 'false', '处理过的列增加\"normalized_\"前缀', '', '4', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '1', '1'), ('108', '18', '18', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('109', '18', '18', '1', '结果表', 'output_table', 'input', '', '', '', '2', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('110', '19', '19', '1', '选择字段', 'selected_cols', 'selectField', '', '默认全选字段', '', '3', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '1', '4', '1'), ('111', '19', '19', '1', '保留原始列', 'keep_original', 'boolean', 'false', '处理过的列增加\"stdized_\"前缀', '', '4', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '3', '0', '1', '1'), ('112', '19', '19', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('113', '19', '19', '1', '结果表', 'output_table', 'input', '', '', '', '2', '{}', '2018-08-29 19:44:30', '2018-08-29 19:44:30', '0', '2', '1', '1', '1'), ('115', '20', '20', '1', '选择字段	', 'selected_cols', 'typeConvered', '', '需要进行类型转换的列', '', '3', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '1', '2', '1'), ('116', '20', '20', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '1', '1', '2'), ('117', '20', '20', '1', '结果表', 'output_table', 'input', '', '', '', '2', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '1', '1', '2'), ('118', '201', '201', '1', '特征列', 'selected_columns', 'selectField', '', '需要进行主成分分析的特征列名', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '1', '4', '1'), ('119', '201', '201', '1', '保留列', 'remain_columns', 'selectField', '', '需要输出到主成分分析结果中的列名', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '0', '4', '1'), ('120', '201', '201', '1', '信息百分比', 'contri_rate', 'floatInput', '0.9', '降维后数据信息保留的百分比', '', '1', '{\"type\":\"float\", \"max\":1, \"min\":0}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '1', '1', '1'), ('121', '201', '201', '1', '训练表', 'input_table', 'input', '', '需要进行主成分分析的数据表，必须包括特征列及标签列', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '1', '1', '1'), ('122', '201', '201', '1', '结果表', 'output_table', 'input', '', '主成分分析的结果', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '1', '1', '1'), ('123', '201', '201', '1', '主成分表', 'eig_output_table', 'input', '', '表示主成分矩阵，每一列都是一个主成分', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '0', '1', '1'), ('124', '202', '202', '1', '待变换特征列', 'selected_cols', 'selectField', '', '需要进行特征变换的列', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '1', '4', '1'), ('125', '202', '202', '1', '变换方法', 'scale_method', 'select', '', '特征变换方法', '', '1', '{\"options\":[{\"key\":\"log2\",\"value\":\"log2\"},{\"key\":\"log10\",\"value\":\"log10\"},{\"key\":\"ln\",\"value\":\"ln\"},{\"key\":\"abs\",\"value\":\"abs\"},{\"key\":\"sqrt\",\"value\":\"sqrt\"}]}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '0', '1', '1'), ('126', '202', '202', '1', '数据表', 'input_table', 'input', '', '需要进行特征变换的数据表	', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '1', '1', '1'), ('127', '202', '202', '1', '结果表', 'output_table', 'input', '', '输出特征变换结果', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '1', '1', '1'), ('128', '203', '203', '1', '待离散列', 'discret_columns', 'selectField', '', '需要进行数据离散的列名', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '1', '4', '1'), ('129', '203', '203', '1', '离散方法', 'discret_method', 'select', 'sameDistance', '目前仅支持等频离散、等距离散', '', '1', '{\"options\":[{\"value\":\"sameDistance\",\"key\":\"等距离散\"},{\"value\":\"sameFrequency\",\"key\":\"等频离散\"}]}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '1', '1', '1'), ('130', '203', '203', '1', '离散区间数	', 'max_bins', 'intInput', '', '数据离散后的个数', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '1', '1', '1'), ('131', '203', '203', '1', '数据表', 'input_table', 'input', '', '需要进行离散的数据表', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '1', '1', '1'), ('132', '203', '203', '1', '结果表', 'output_table', 'input', '', '结果表', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '0', '1', '1'), ('133', '204', '204', '1', '待平滑列', 'selected_cols', 'selectField', '', '需要进行异常平滑的列名', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '1', '4', '1'), ('134', '204', '204', '1', '平滑方法', 'soften_method', 'select', 'zscore', '平滑方法，支持Zscore平滑、百分位平滑和阈值平滑', '', '1', '{\"options\":[{\"value\":\"zscore\",\"key\":\"Zscore平滑\"},{\"value\":\"min_max_thresh\",\"key\":\"阈值平滑\"},{\"value\":\"min_max_per\",\"key\":\"百分位平滑\"}]}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '0', '1', '1'), ('135', '204', '204', '1', '置信水平', 'cl', 'intInput', '10', '当平滑方法是zscore时生效', '', '1', '{\"type\":\"int\", \"max\":100, \"min\":0}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '0', '1', '1'), ('136', '204', '204', '1', '最低百分位', 'min_per', 'intInput', '0.0', '仅当平滑方法为百分位平滑时生效', '', '1', '{\"type\":\"float\", \"max\":1.0, \"min\":0.0}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '0', '1', '1'), ('137', '204', '204', '1', '最高百分位', 'max_per', 'intInput', '1.0', '仅当平滑方法为百分位平滑时生效', '', '1', '{\"type\":\"float\", \"max\":1.0, \"min\":0.0}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '0', '1', '1'), ('138', '204', '204', '1', '阈值最小值', 'min_thresh', 'floatInput', '-9999', '仅当平滑方法为阈值平滑时生效', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '0', '1', '1'), ('139', '204', '204', '1', '阈值最大值', 'max_thresh', 'floatInput', '9999', '仅当平滑方法为阈值平滑时生效', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '3', '0', '1', '1'), ('140', '204', '204', '1', '数据表', 'input_table', 'input', '', '需要进行异常平滑处理的数据表', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '1', '1', '1'), ('141', '204', '204', '1', '结果表', 'output_table', 'input', '', '经过异常平滑处理的结果数据表', '', '1', '{}', '2018-09-11 17:13:52', '2018-09-11 17:13:52', '0', '2', '1', '1', '1'), ('142', '1002', '1002', '1', '预测输入', 'predict_input', 'input', null, null, null, '1', '{}', '2018-10-17 11:31:23', '2018-10-17 11:31:23', '0', '2', '0', '1', '1'), ('143', '1003', '1003', '1', '预测输出', 'predict_output', 'input', null, null, null, '1', '{}', '2018-10-17 11:34:07', '2018-10-17 11:34:07', '0', '2', '0', '1', '1'), ('145', '6', '6', '1', '保留字段', 'remain_cols', 'selectField', '', '', '', '2', '{}', '2018-10-19 12:20:52', '2018-10-19 12:20:52', '0', '3', '0', '4', '1'), ('162', '702', '702', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-10-31 19:39:34', '2018-10-31 19:39:34', '0', '2', '1', '1', '1'), ('163', '702', '702', '1', '输出表', 'output_table', 'input', '', '', '', '1', '{}', '2018-10-31 19:39:34', '2018-10-31 19:39:34', '0', '2', '0', '1', '1'), ('164', '702', '702', '1', '标识文章id的列名', 'doc_id', 'selectField', '', '', '', '1', '{\"singleField\":true}', '2018-10-31 19:39:34', '2018-10-31 19:39:34', '0', '3', '1', '4', '1'), ('165', '702', '702', '1', '标识文章中word的列名', 'doc_word', 'selectField', '', '', '', '1', '{\"singleField\":true}', '2018-10-31 19:39:34', '2018-10-31 19:39:34', '0', '3', '1', '4', '1'), ('166', '702', '702', '1', '标识文章中word个数的列名', 'doc_count', 'selectField', '', '', '', '1', '{\"singleField\":true}', '2018-10-31 19:39:34', '2018-10-31 19:39:34', '0', '3', '1', '4', '1'), ('167', '7', '7', '1', '保留列', 'remain_cols', 'selectField', null, null, '选择的列将原值保留到输出表，如果和索引化列重复则保留索引化后的结果', '2', '{}', '2018-11-01 10:17:08', '2018-11-01 10:17:08', '0', '3', '0', '4', '1'), ('168', '7', '7', '1', '索引输出表', 'index_table', 'input', '', null, null, '0', '{}', '2018-11-01 10:23:00', '2018-11-01 10:23:00', '0', '2', '0', '1', '2'), ('169', '701', '701', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '2', '1', '1', '1'), ('170', '701', '701', '1', '输出表', 'output_table', 'input', '', '', '', '1', '{}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '2', '0', '1', '1'), ('171', '701', '701', '1', '单词列', 'sent_col_name', 'selectField', '', '', '', '1', '{\"singleField\":true}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '3', '1', '4', '1'), ('172', '701', '701', '1', '单词的特征维度', 'layer_size', 'intInput', '100', '', '', '1', '{\"type\":\"int\",\"max\":1000,\"min\":0}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '3', '1', '1', '1'), ('173', '701', '701', '1', '截断的最小词频', 'min_count', 'intInput', '5', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '3', '1', '1', '1'), ('174', '701', '701', '1', '输出wordvec表的分区数', 'num_partitions', 'intInput', '4', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '3', '1', '1', '1'), ('175', '701', '701', '1', '开始学习速率', 'alpha', 'floatInput', '0.025', '', '', '1', '{\"type\":\"float\",\"min\":0.0}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '3', '1', '1', '1'), ('176', '701', '701', '1', '训练的迭代次数', 'iter_train', 'intInput', '5', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '3', '1', '1', '1'), ('177', '701', '701', '1', '随机数种子', 'seed', 'intInput', '0', '', '', '1', '{\"type\":\"int\",\"min\":0}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '3', '1', '1', '1'), ('178', '701', '701', '1', '单词窗口大小', 'window', 'intInput', '5', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '3', '1', '1', '1'), ('179', '701', '701', '1', 'sentence的最大长度', 'max_sentence_length', 'intInput', '4000', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-02 15:09:27', '2018-11-02 15:09:27', '0', '3', '1', '1', '1'), ('191', '704', '704', '1', '输入表名', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-02 15:29:17', '2018-11-02 15:29:17', '0', '2', '1', '1', '1'), ('192', '704', '704', '1', '输出词频统计表名', 'output_table', 'input', '', '', '', '1', '{}', '2018-11-02 15:29:17', '2018-11-02 15:29:17', '0', '2', '0', '1', '1'), ('193', '704', '704', '1', '相似度计算中第一列的列名', 'inputSelectedColName1', 'selectField', '', '', '', '1', '{}', '2018-11-02 15:29:17', '2018-11-02 15:29:17', '0', '3', '1', '4', '1'), ('194', '704', '704', '1', '相似度计算中第二列的列名', 'inputSelectedColName2', 'selectField', '', '', '', '1', '{}', '2018-11-02 15:29:17', '2018-11-02 15:29:17', '0', '3', '1', '4', '1'), ('195', '704', '704', '1', '输出表追加的列名', 'inputAppendColNames', 'selectField', '', '', '', '1', '{}', '2018-11-02 15:29:17', '2018-11-02 15:29:17', '0', '3', '1', '4', '1'), ('196', '704', '704', '1', '输出表中相似度列的列名', 'outputColName', 'input', 'output', '', '', '1', '{}', '2018-11-02 15:29:17', '2018-11-02 15:29:17', '0', '3', '1', '1', '1'), ('197', '704', '704', '1', '相似度计算方法', 'method', 'select', 'cosine_sim', '', '', '1', '{\"options\":[{\"key\":\"cosine_sim\",\"value\":\"cosine_sim\"},\n{\"key\":\"euclidean_dis\",\"value\":\"euclidean_dis\"},{\"key\":\"simhash_hamming_dis\",\"value\":\"simhash_hamming_dis\"},{\"key\":\"simhash\",\"value\":\"simhash\"},\n{\"key\":\"simhash_hamming_sim\",\"value\":\"simhash_hamming_sim\"},{\"key\":\"jaccard_dis\",\"value\":\"jaccard_dis\"},{\"key\":\"jaccard_sim\",\"value\":\"jaccard_sim\"},\n{\"key\":\"levenshtein_dis\",\"value\":\"levenshtein_dis\"},{\"key\":\"levenshtein_sim\",\"value\":\"levenshtein_sim\"},{\"key\":\"loncomsubsequence_dis\",\"value\":\"loncomsubsequence_dis\"},\n{\"key\":\"loncomsubsequence_sim\",\"value\":\"loncomsubsequence_sim\"},{\"key\":\"loncomsubstr_dis\",\"value\":\"loncomsubstr_dis\"},{\"key\":\"loncomsubstr_sim\",\"value\":\"loncomsubstr_sim\"}\n]}', '2018-11-02 15:29:17', '2018-11-02 15:29:17', '0', '3', '1', '1', '1'), ('198', '705', '705', '1', '输入表名', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-02 15:54:37', '2018-11-02 15:54:37', '0', '2', '1', '1', '1'), ('199', '705', '705', '1', '输出表', 'output_table', 'input', '', '', '', '1', '{}', '2018-11-02 15:54:37', '2018-11-02 15:54:37', '0', '2', '0', '1', '1'), ('200', '705', '705', '1', '分词好的文档列名，分词用空格隔开', 'doc_col', 'selectField', '', '', '', '1', '{}', '2018-11-02 15:54:37', '2018-11-02 15:54:37', '0', '3', '1', '4', '1'), ('201', '705', '705', '1', '窗口大小', 'window_size', 'intInput', '', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-02 15:54:37', '2018-11-02 15:54:37', '0', '3', '1', '1', '1'), ('202', '705', '705', '1', '截断的最小词频', 'min_count', 'intInput', '', '', '', '1', '{\"type\":\"int\",\"min\":0}', '2018-11-02 15:54:37', '2018-11-02 15:54:37', '0', '3', '1', '1', '1'), ('203', '9', '9', '1', '特征列', 'feature_cols', 'selectField', null, null, null, '1', '{}', '2018-11-02 16:31:53', '2018-11-02 16:31:53', '0', '3', '1', '4', '1'), ('204', '9', '9', '1', '标签列', 'label_col', 'selectField', null, null, null, '1', '{}', '2018-11-02 16:33:17', '2018-11-02 16:33:17', '0', '3', '1', '4', '1'), ('205', '706', '706', '1', '输入表名', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '2', '1', '1', '1'), ('206', '706', '706', '1', '输出词表', 'outword_table', 'input', '', '', '', '1', '{}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '2', '0', '1', '1'), ('207', '706', '706', '1', '输出文档向量表', 'outvoc_table', 'input', '', '', '', '1', '{}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '2', '0', '1', '1'), ('208', '706', '706', '1', '输出词向量表', 'outdoc_table', 'input', '', '', '', '1', '{}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '2', '0', '1', '1'), ('209', '706', '706', '1', '文档id列名', 'doc_id_name', 'selectField', '', '', '', '1', '{}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '4', '1'), ('210', '706', '706', '1', '文档内容，可以是用分词的结果，空格分割', 'doc_col_name', 'selectField', '', '', '', '1', '{}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '4', '1'), ('211', '706', '706', '1', '单词的特征维度', 'layer_size', 'intInput', '100', '', '', '1', '{\"type\":\"int\",\"min\":0,\"max\":1000}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '1', '1'), ('212', '706', '706', '1', '语言模型', 'c_bow', 'select', '0', '', '', '1', '{\"options\":[{\"key\":\"cbow模型\",\"value\":1},{\"key\":\"skip-gram模型\",\"value\":0}]}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '1', '1'), ('213', '706', '706', '1', '单词窗口大小', 'window', 'intInput', '5', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '1', '1'), ('214', '706', '706', '1', '截断的最小词频', 'min_count', 'intInput', '5', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '1', '1'), ('215', '706', '706', '1', '是否采用HIERARCHICAL SOFTMAX', 'hs', 'select', '0', '', '', '1', '{\"options\":[{\"key\":\"采用\",\"value\":1},{\"key\":\"不采用\",\"value\":0}]}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '1', '1'), ('216', '706', '706', '1', '向下采样阈值', 'sample', 'intInput', '0', '', '', '1', '{\"type\":\"int\",\"max\":0}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '1', '1'), ('217', '706', '706', '1', '开始学习速率', 'alpha', 'floatInput', '0.025', '', '', '1', '{\"type\":\"float\",\"min\":0}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '1', '1'), ('218', '706', '706', '1', '训练的迭代次数', 'iter_train', 'intInput', '1', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '1', '1'), ('219', '706', '706', '1', 'window是否随机	', 'random_window', 'select', '1', '', '', '1', '{\"options\":[{\"key\":\"表示大小在1～5间随机\",\"value\":1},{\"key\":\"不随机，其值由window参数指定	\",\"value\":0}]}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '1', '1'), ('220', '706', '706', '1', '负采样个数', 'negative', 'intInput', '0', '', '', '1', '{}', '2018-11-02 17:21:12', '2018-11-02 17:21:12', '0', '3', '1', '1', '1'), ('221', '708', '708', '1', '输入表名', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-02 21:28:36', '2018-11-02 21:28:36', '0', '2', '1', '1', '1'), ('222', '708', '708', '1', '输出词表', 'output_table', 'input', '', '', '', '1', '{}', '2018-11-02 21:28:36', '2018-11-02 21:28:36', '0', '2', '0', '1', '1'), ('223', '708', '708', '1', '选择什么类型的分词方法', 'which_method', 'select', 'jieba', '', '', '1', '{\"options\":[{\"key\":\"jieba\",\"calue\":\"jieba\"}]}', '2018-11-02 21:28:36', '2018-11-02 21:28:36', '0', '3', '1', '1', '1'), ('224', '708', '708', '1', '需要进行分词处理的列名', 'selected_cols', 'selectField', '', '', '', '1', '{}', '2018-11-02 21:28:36', '2018-11-02 21:28:36', '0', '3', '1', '4', '1'), ('225', '708', '708', '1', '是否有词性标注', 'part_of_speech', 'boolean', 'false', '', '', '1', '{}', '2018-11-02 21:28:36', '2018-11-02 21:28:36', '0', '3', '1', '1', '1'), ('226', '709', '709', '1', '输入表名', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-05 16:32:10', '2018-11-05 16:32:10', '0', '2', '1', '1', '1'), ('227', '709', '709', '1', '停用词表', 'stopwords_table', 'dataPreview', '', '', '', '1', '{}', '2018-11-05 16:32:10', '2018-11-05 16:32:10', '0', '3', '1', '1', '1'), ('228', '709', '709', '1', '输出词表', 'output_table', 'input', '', '', '', '1', '{}', '2018-11-05 16:32:10', '2018-11-05 16:32:10', '0', '2', '0', '1', '1'), ('229', '709', '709', '1', '文档id列名', 'doc_id_col', 'selectField', '', '', '', '1', '{}', '2018-11-05 16:32:10', '2018-11-05 16:32:10', '0', '3', '1', '4', '1'), ('230', '709', '709', '1', '文本列', 'txt_col', 'selectField', '', '', '', '1', '{}', '2018-11-05 16:32:10', '2018-11-05 16:32:10', '0', '3', '1', '4', '1'), ('231', '709', '709', '1', '摘要的句子个数', 'top_n', 'intInput', '5', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-05 16:32:10', '2018-11-05 16:32:10', '0', '3', '1', '1', '1'), ('232', '709', '709', '1', '阻尼系数', 'damping_factor', 'floatInput', '0.85', '', '', '1', '{\"type\":\"float\",\"min\":0,\"max\":1}', '2018-11-05 16:32:10', '2018-11-05 16:32:10', '0', '3', '1', '1', '1'), ('233', '709', '709', '1', '迭代最大次数', 'max_iter', 'intInput', '100', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-05 16:32:10', '2018-11-05 16:32:10', '0', '3', '1', '1', '1'), ('234', '709', '709', '1', '收敛系数', 'epsilon', 'floatInput', '0.000001', '', '', '1', '{\"type\":\"float\",\"min\":0}', '2018-11-05 16:32:10', '2018-11-05 16:32:10', '0', '3', '1', '1', '1'), ('235', '709', '709', '1', '文章摘要处理方法', 'method', 'select', 'textrank', '', '', '1', '{\"options\":[{\"key\":\"textrank\",\"value\":\"textrank\"},{\"key\":\"cluster_avg_std\",\"value\":\"cluster_avg_std\"},{\"key\":\"cluster_topn\",\"value\":\"cluster_topn\"}]}', '2018-11-05 16:32:10', '2018-11-05 16:32:10', '0', '3', '1', '1', '1'), ('252', '206', '206', '1', '数据表', 'input_table', 'input', '', '用于评估特征重要性的数据表，需要包含模型训练时所需要的特征及标签', '', '1', '{}', '2018-11-05 20:33:31', '2018-11-05 20:33:31', '0', '2', '1', '1', '1'), ('253', '206', '206', '1', '特征重要性结果表', 'output_table', 'input', '', '各个特征的重要性结果', '', '1', '{}', '2018-11-05 20:33:31', '2018-11-05 20:33:31', '0', '2', '1', '1', '1'), ('254', '206', '206', '1', '标签列', 'label_col', 'selectField', '', '训练线性模型时使用的标签列', '', '1', '{\"limitSize\":1}', '2018-11-05 20:33:31', '2018-11-05 20:33:31', '0', '3', '1', '4', '1'), ('255', '206', '206', '1', '输入模型路径', 'model_name', 'input', '', '使用线性回归组件及逻辑回归分类组件(算法类别参数为binomial时)生成的模型路径', '', '1', '{}', '2018-11-05 20:33:31', '2018-11-05 20:33:31', '0', '2', '1', '1', '1'), ('256', '206', '206', '1', '特征列', 'feature_cols', 'selectField', '', '训练线性模型时使用的特征列名', '', '1', '{\"singleField\":false}', '2018-11-05 20:33:31', '2018-11-05 20:33:31', '0', '3', '1', '4', '1'), ('258', '206', '206', '1', '特征是否稀疏', 'enable_sparse', 'boolean', 'false', '特征列是否为稀疏存储', '', '1', '{}', '2018-11-05 20:33:31', '2018-11-05 20:33:31', '0', '3', '0', '1', '1'), ('259', '206', '206', '1', '项分隔符', 'item_delimiter', 'input', ' ', '键值对与键值对之间的分隔符。若特征为稀疏存储，才可以对其进行设置', '', '1', '{}', '2018-11-05 20:33:31', '2018-11-05 20:33:31', '0', '3', '0', '1', '1'), ('260', '206', '206', '1', '键值对分隔符', 'kv_delimiter', 'input', ':', '键与值之间的分隔符。若特征为稀疏存储，才可以对其进行设置', '', '1', '{}', '2018-11-05 20:33:31', '2018-11-05 20:33:31', '0', '3', '0', '1', '1'), ('261', '804', '804', '1', '训练表', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '训练数据表，必须包括特征列及标签列', '1', '{}', '2018-11-05 21:13:08', '2018-11-05 21:13:08', '0', '2', '1', '1', '1'), ('262', '804', '804', '1', '模型路径', 'model_name', 'input', '', '模型训练完成后在HDFS上的存储路径', '模型训练完成后在HDFS上的存储路径', '4', '{}', '2018-11-05 21:13:08', '2018-11-05 21:13:08', '0', '2', '1', '1', '1'), ('263', '804', '804', '1', '标签列', 'label_col', 'selectField', '', '训练表中的标签列', '训练表中的标签列', '3', '{\"limitSize\":1}', '2018-11-05 21:13:08', '2018-11-05 21:13:08', '0', '3', '1', '4', '1'), ('265', '804', '804', '1', '特征列', 'feature_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '2', '{}', '2018-11-05 21:13:08', '2018-11-05 21:13:08', '0', '3', '0', '4', '1'), ('272', '804', '804', '1', '树深度', 'max_depth', 'intInput', '5', '决策树的最大深度，可以通过该参数控制模型的复杂度', '决策树的最大深度，可以通过该参数控制模型的复杂度', '6', '{\"type\":\"int\",\"min\":1}', '2018-11-05 21:13:08', '2018-11-05 21:13:08', '0', '3', '0', '1', '1'), ('274', '804', '804', '1', '随机种子', 'seed', 'intInput', '', '生成随机数的随机种子', '生成随机数的随机种子', '12', '{\"type\":\"int\",\"min\":1000,\"max\":1000000}', '2018-11-05 21:13:08', '2018-11-05 21:13:08', '0', '3', '0', '1', '1'), ('275', '804', '804', '1', '树个数', 'num_trees', 'intInput', '20', '随机森林算法中待训练的树的数量', '随机森林算法中待训练的树的数量', '5', '{\"type\":\"int\",\"min\":1000,\"max\":1000}', '2018-11-05 21:13:08', '2018-11-05 21:13:08', '0', '3', '1', '1', '1'), ('276', '806', '806', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-05 21:25:29', '2018-11-05 21:25:29', '0', '2', '1', '1', '1'), ('277', '806', '806', '1', '输出的模型名', 'model_name', 'input', '', '', '', '1', '{}', '2018-11-05 21:25:29', '2018-11-05 21:25:29', '0', '2', '1', '1', '1'), ('278', '806', '806', '1', '评估结果表', 'output_table', 'input', '', '', '', '1', '', '2018-11-05 21:25:29', '2018-11-05 21:25:29', '0', '3', '1', '1', '1'), ('279', '806', '806', '1', '输入表中权重列的列名', 'selected_cols', 'selectField', '', '', '', '1', '{}', '2018-11-05 21:25:29', '2018-11-05 21:25:29', '0', '3', '0', '4', '1'), ('280', '205', '205', '1', '数据表', 'input_table', 'input', '', '用于评估特征重要性的数据表，需要包含模型训练时所需要的特征及标签', '', '1', '{}', '2018-11-06 11:05:40', '2018-11-06 11:05:40', '0', '2', '1', '1', '1'), ('281', '205', '205', '1', '特征重要性结果表', 'output_table', 'input', '', '各个特征的重要性结果', '', '1', '{}', '2018-11-06 11:05:40', '2018-11-06 11:05:40', '0', '2', '1', '1', '1'), ('283', '205', '205', '1', '输入模型路径', 'model_name', 'input', '', '使用随机森林分类或随机森林回归组件生成的模型路径', '', '1', '{}', '2018-11-06 11:05:40', '2018-11-06 11:05:40', '0', '2', '1', '1', '1'), ('307', '208', '208', '1', '数据表', 'input_table', 'input', '', '用于评估特征重要性的数据表，需要包含模型训练时所需要的特征及标签	', '', '1', '{}', '2018-11-07 13:22:54', '2018-11-07 13:22:54', '0', '2', '1', '1', '1'), ('308', '208', '208', '1', '特征重要性结果表	', 'output_table', 'input', '', '各个特征的重要性结果', '', '1', '{}', '2018-11-07 13:22:54', '2018-11-07 13:22:54', '0', '2', '1', '1', '1'), ('309', '208', '208', '1', '标签列', 'label_col', 'selectField', '', '训练线性模型时使用的标签列', '', '1', '{\"limitSize\":1}', '2018-11-07 13:22:54', '2018-11-07 13:22:54', '0', '3', '1', '4', '1'), ('310', '208', '208', '1', '输入模型路径', 'model_name', 'input', '', '使用GBDT回归和GBDT二分类生成的模型路径', '', '1', '{}', '2018-11-07 13:22:54', '2018-11-07 13:22:54', '0', '2', '1', '1', '1'), ('311', '208', '208', '1', '特征列', 'feature_cols', 'selectField', '', '训练线性模型时使用的特征列名', '', '1', '{}', '2018-11-07 13:22:54', '2018-11-07 13:22:54', '0', '3', '1', '4', '1'), ('462', '805', '805', '1', '输入表名', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '训练数据表，必须包括特征列及标签列', '1', '{}', '2018-11-08 20:18:57', '2018-11-08 20:18:57', '0', '2', '1', '1', '1'), ('463', '805', '805', '1', '特征列', 'feature_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '2', '{}', '2018-11-08 20:18:57', '2018-11-08 20:18:57', '0', '3', '1', '4', '1'), ('464', '805', '805', '1', '标签列', 'label_col', 'selectField', '', '训练表中的标签列', '训练表中的标签列', '3', '{}', '2018-11-08 20:18:57', '2018-11-08 20:18:57', '0', '3', '1', '4', '1'), ('466', '805', '805', '1', '模型路径', 'model_name', 'output_model', '', '模型训练完成后在HDFS上的存储路径', '模型训练完成后在HDFS上的存储路径', '4', '{\"limitSize\":1}', '2018-11-08 20:18:57', '2018-11-08 20:18:57', '0', '2', '1', '1', '1'), ('468', '805', '805', '1', '损失函数类型', 'loss_type', 'select', 'squared', '损失函数类型', '损失函数类型', '5', '{\"options\":[{\"key\":\"squared\",\"value\":\"squared\"},{\"key\":\"absolute\",\"value\":\"absolute\"}]}', '2018-11-08 20:18:57', '2018-11-08 20:18:57', '0', '3', '0', '1', '1'), ('472', '805', '805', '1', '子节点最小个数', 'min_instances_per_node', 'intInput', '500', '节点分割后子节点的最小个数，若分割后小于该数量，则取消分割', '节点分割后子节点的最小个数，若分割后小于该数量，则取消分割', '12', '{\"type\":\"int\",\"min\":1,\"max\":1000}', '2018-11-08 20:18:57', '2018-11-08 20:18:57', '0', '3', '0', '1', '1'), ('473', '805', '805', '1', '数据采样比例', 'subsampling_rate', 'floatInput', '0.6', '训练每个决策树的数据占总训练数据的比例', '训练每个决策树的数据占总训练数据的比例', '16', '{\"type\":\"float\",\"min\":0,\"max\":1}', '2018-11-08 20:18:57', '2018-11-08 20:18:57', '0', '3', '0', '1', '1'), ('477', '805', '805', '1', '随机种子', 'seed', 'intInput', '0', '生成随机数的随机种子', '生成随机数的随机种子', '14', '{\"type\":\"float\",\"min\":0,\"max\":10}', '2018-11-08 20:18:57', '2018-11-08 20:18:57', '0', '3', '0', '1', '1'), ('480', '805', '805', '1', '迭代次数', 'max_iter', 'intInput', '20', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '6', '{\"type\":\"int\",\"min\":0}', '2018-11-08 20:18:57', '2018-11-08 20:18:57', '0', '3', '0', '1', '1'), ('481', '805', '805', '1', '树深度', 'max_depth', 'intInput', '5', '决策树的最大深度，可以通过该参数控制模型的复杂度', '决策树的最大深度，可以通过该参数控制模型的复杂度', '7', '{\"type\":\"int\",\"min\":1,\"max\":100}', '2018-11-08 20:18:57', '2018-11-08 20:18:57', '0', '3', '0', '1', '1'), ('482', '502', '502', '1', '训练表', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '训练数据表，必须包括特征列及标签列', '1', '{}', '2018-11-08 20:23:19', '2018-11-08 20:23:19', '0', '2', '1', '1', '1'), ('484', '502', '502', '1', '特征列', 'selected_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '2', '{}', '2018-11-08 20:23:19', '2018-11-08 20:23:19', '0', '3', '1', '4', '1'), ('485', '502', '502', '1', '标签列', 'label_col', 'selectField', '', '训练表中的标签列', '训练表中的标签列', '3', '{}', '2018-11-08 20:23:19', '2018-11-08 20:23:19', '0', '3', '1', '4', '1'), ('486', '502', '502', '1', '模型路径', 'model_name', 'input', '', '模型训练完成后在HDFS上的存储路径', '模型训练完成后在HDFS上的存储路径', '5', '{}', '2018-11-08 20:23:19', '2018-11-08 20:23:19', '0', '2', '1', '1', '1'), ('487', '502', '502', '1', '模型类型', 'model_type', 'input', 'multinomial', '选择模型类型，multinomial为多项式模型，bernoulli为伯努利模型', '选择模型类型，multinomial为多项式模型，bernoulli为伯努利模型', '6', '{}', '2018-11-08 20:23:19', '2018-11-08 20:23:19', '0', '3', '0', '1', '1'), ('488', '502', '502', '1', '平滑因子', 'smoothing', 'input', '1.0', 'NB计算概率时的平滑参数', 'NB计算概率时的平滑参数', '7', '{}', '2018-11-08 20:23:19', '2018-11-08 20:23:19', '0', '3', '0', '1', '1'), ('489', '502', '502', '1', '分类阈值', 'thresholds', 'input', null, '分类阈值需要逗号分隔的字符串类型，阈值个数必须等于分类个数，eg,\'1.0,1.0\'，\r\n\r\n最终的分类是根据p/t的最大值判断，其中p为真实概率，t为该类的阈值', '分类阈值需要逗号分隔的字符串类型，阈值个数必须等于分类个数，eg,\'1.0,1.0\'，\r\n\r\n最终的分类是根据p/t的最大值判断，其中p为真实概率，t为该类的阈值', '8', '{}', '2018-11-08 20:23:19', '2018-11-08 20:23:19', '0', '3', '0', '1', '1'), ('491', '508', '508', '1', '评估数据表', 'input_table', 'input', '', '用于评估聚类模型的数据表，其中必须包含训练聚类模型时所使用的特征，要注意特征名称及数据含义的一致性，否则结果无法预估', '', '1', '{}', '2018-11-08 20:34:33', '2018-11-08 20:34:33', '0', '2', '1', '1', '1'), ('492', '508', '508', '1', '评估结果表', 'output_table', 'input', '', '聚类模型评估结果，包括Calinski-Harabasz指标及各类别包含数据量', '', '3', '{}', '2018-11-08 20:34:33', '2018-11-08 20:34:33', '0', '2', '1', '1', '1'), ('493', '508', '508', '1', '聚类模型', 'model_name', 'input', '', '使用Kmeans、高斯混合聚类组件训练出的模型', '', '2', '{}', '2018-11-08 20:34:33', '2018-11-08 20:34:33', '0', '2', '1', '1', '1'), ('494', '508', '508', '1', '聚类模型使用的列', 'selected_cols', 'selectField', '', '', '', '1', '{}', '2018-11-08 20:34:33', '2018-11-08 20:34:33', '1', '3', '0', '4', '1'), ('535', '711', '711', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-08 21:35:22', '2018-11-08 21:35:22', '0', '2', '1', '1', '1'), ('536', '711', '711', '1', '输出表', 'output_table', 'input', '', '', '', '1', '{}', '2018-11-08 21:35:22', '2018-11-08 21:35:22', '0', '2', '1', '1', '1'), ('538', '711', '711', '1', '标识文章id的列名', 'doc_id_col', 'selectField', '', '', '', '1', '{\"singleField\":true}', '2018-11-08 21:35:22', '2018-11-08 21:35:22', '0', '3', '1', '4', '1'), ('539', '711', '711', '1', 'Word列', 'doc_content_col', 'selectField', '', '', '', '1', '{\"singleField\":true}', '2018-11-08 21:35:22', '2018-11-08 21:35:22', '0', '3', '1', '4', '1'), ('540', '711', '711', '1', '输出前多少个关键词', 'top_n', 'intInput', '5', '', '', '1', '{}', '2018-11-08 21:35:22', '2018-11-08 21:35:22', '0', '3', '0', '1', '1'), ('541', '711', '711', '1', 'TextRank算法的窗口大小', 'window_size', 'intInput', '2', '', '', '1', '{}', '2018-11-08 21:35:22', '2018-11-08 21:35:22', '0', '3', '0', '1', '1'), ('542', '711', '711', '1', 'TextRank算法的阻尼系数', 'dumping_factor', 'floatInput', '0.85', '', '', '1', '{}', '2018-11-08 21:35:22', '2018-11-08 21:35:22', '0', '3', '0', '1', '1'), ('543', '711', '711', '1', 'TextRank算法的最大迭代次数', 'max_iter', 'intInput', '100', '', '', '1', '{}', '2018-11-08 21:35:22', '2018-11-08 21:35:22', '0', '3', '0', '1', '1'), ('544', '711', '711', '1', 'TextRank算法的收敛残差阈值', 'epsilon', 'floatInput', '0.000001', '', '', '1', '{}', '2018-11-08 21:35:22', '2018-11-08 21:35:22', '0', '3', '0', '1', '1'), ('574', '803', '803', '1', '训练表', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '训练数据表，必须包括特征列及标签列', '1', '{}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '2', '1', '1', '1'), ('576', '803', '803', '1', '特征列', 'selected_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '2', '{}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '1', '4', '1'), ('577', '803', '803', '1', '树深度', 'max_depth', 'intInput', '5', '决策树的最大深度，可以通过该参数控制模型的复杂度', '决策树的最大深度，可以通过该参数控制模型的复杂度', '6', '{\"type\":\"int\",\"min\":1,\"max\":100}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '1', '1', '1'), ('578', '803', '803', '1', '标签列', 'label_col', 'selectField', '', '训练表中的标签列', '训练表中的标签列', '3', '{}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '1', '4', '1'), ('579', '803', '803', '1', '模型路径', 'model_name', 'output', '', '模型训练完成后在HDFS上的存储路径', '模型训练完成后在HDFS上的存储路径', '4', '{\"limitSize\":1}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '2', '1', '1', '1'), ('580', '803', '803', '1', '子节点最小个数', 'mininstances_pernode', 'input', '1', '节点分割后子节点的最小个数，若分割后小于该数量，则取消分割。', '节点分割后子节点的最小个数，若分割后小于该数量，则取消分割。', '10', '{\"type\":\"int\",\"min\":1}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '0', '1', '1'), ('581', '803', '803', '1', '分割最小信息增益', 'mininfo_gain', 'floatInput', '0.0', '树节点分割时的最小信息增益', '树节点分割时的最小信息增益', '7', '{}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '0', '1', '1'), ('582', '803', '803', '1', '聚合时的最大内存', 'maxmemory_inmb', 'input', '256', '数据聚合时所分配的最大内存，单位为MB，如果值太小，则聚合时可能出错', '数据聚合时所分配的最大内存，单位为MB，如果值太小，则聚合时可能出错', '8', '{\"type\":\"int\",\"min\":1}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '0', '1', '1'), ('583', '803', '803', '1', '是否缓存实例ID', 'cachenode_ids', 'boolean', 'False', '如果是，则对每一个实例都缓存node ID, 加速训练。\r\n\r\n可以通过checkpoint_interval配置多久缓存一次，或者禁用', '如果是，则对每一个实例都缓存node ID, 加速训练。\r\n\r\n可以通过checkpoint_interval配置多久缓存一次，或者禁用', '9', '{}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '0', '1', '1'), ('584', '803', '803', '1', '检查点间隔', 'checkpoint_interval', 'input', '10', '设置检查间隔(>=1)或禁用(-1)；例如，设置为10，则每10次迭代会缓存一次配置', '设置检查间隔(>=1)或禁用(-1)；例如，设置为10，则每10次迭代会缓存一次配置', '11', '{}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '0', '1', '1'), ('585', '803', '803', '1', '信息增益计算方法', 'impurity', 'select', 'gini', '用于计算信息增益的方法', '用于计算信息增益的方法', '5', '{\"options\":[{\"key\":\"entropy\",\"value\":\"entropy\"},{\"key\":\"gini\",\"value\":\"gini\"}]}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '0', '1', '1'), ('586', '803', '803', '1', '随机种子', 'seed', 'input', '', '生成随机数的随机种子', '生成随机数的随机种子', '12', '{}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '0', '1', '1'), ('587', '803', '803', '1', '分箱数', 'max_bins', 'input', '32', '离散连续特征时的最大分箱数，需要大于等于2并且大于等于离散特征的分类数', '离散连续特征时的最大分箱数，需要大于等于2并且大于等于离散特征的分类数', '13', '{\"type\":\"int\",\"min\":2}', '2018-11-08 21:56:45', '2018-11-08 21:56:45', '0', '3', '0', '1', '1'), ('588', '102', '102', '1', '输入表', 'input_table', 'select', '', '必选', '', '1', '{}', '2018-11-09 11:31:52', '2018-11-09 11:31:52', '0', '2', '1', '1', '1'), ('589', '102', '102', '1', '输出表', 'output_table', 'input', '', '必选', '', '2', '{}', '2018-11-09 11:31:52', '2018-11-09 11:31:52', '0', '2', '1', '1', '1'), ('590', '102', '102', '1', '输入表分区', 'input_partition', 'select', '', '可选', '', '3', '{}', '2018-11-09 11:31:52', '2018-11-09 11:31:52', '0', '2', '0', '1', '1'), ('591', '101', '101', '1', '输入表', 'input_table', 'input', '', '必选', '', '1', '{}', '2018-11-09 11:33:15', '2018-11-09 11:33:15', '0', '2', '1', '1', '1'), ('592', '101', '101', '1', '输出表', 'output_table', 'select', '', '必选', '', '2', '{}', '2018-11-09 11:33:15', '2018-11-09 11:33:15', '0', '2', '1', '1', '1'), ('593', '101', '101', '1', '输出分区', 'output_partition', 'select', '', '必选', '', '3', '{}', '2018-11-09 11:33:15', '2018-11-09 11:33:15', '0', '2', '1', '1', '1'), ('594', '600', '90001', '1', '版本', '__launchVenv', 'select', 'tensorflow1.7_py2', '', '', '1', '{\"options\":[{\"value\":\"tensorflow1.2_py2\",\"key\":\"V1.2(python2)\"},{\"value\":\"tensorflow1.7_py2\",\"key\":\"V1.7(python2)\"},{\"value\":\"tensorflow-gpu1.7_py2\",\"key\":\"V1.7 for GPU(python2)\"},{\"value\":\"tensorflow1.9_py2\",\"key\":\"V1.9(python2)\"},{\"value\":\"tensorflow-gpu1.9_py2\",\"key\":\"V1.9 for GPU(python2)\"}]}', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0', '3', '1', '1', '1'), ('595', '600', '90001', '1', 'Python代码文件', '__codeFiles', 'codeUploader', '', '支持.py文件和.tgz、.tar、.zip、.tar.gz后缀的压缩文件', '', '1', '', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0', '3', '1', '1', '1'), ('596', '600', '90001', '1', 'Python主文件', '__launchFile', 'input', '', '如果上传压缩包填写相对压缩包内部目录结构的相对路径', '', '1', '', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0', '3', '1', '1', '1'), ('597', '600', '90001', '1', '超参配置', '__launchArgs', 'keyValue', '', '超参配置将在运行时通过命令行参数传入启动脚本', '', '1', '', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0', '3', '0', '1', '1'), ('598', '600', '90001', '1', '框架类型', 'app-type', 'input', 'tensorflow', '表示框架类型，不再页面展示', '', '1', '', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0', '2', '1', '1', '1'), ('599', '600', '90001', '1', '样本数据', 'input_path', 'input', '', '样本数据，端点不再页面展示', '', '1', '', '2018-11-09 11:40:43', '2018-11-09 11:40:43', '0', '2', '1', '1', '1'), ('600', '600', '90001', '1', '模型路径', 'output_path', 'input', '', '模型路径，端点不再页面展示', '', '1', '', '2018-11-09 11:40:43', '2018-11-09 11:40:43', '0', '2', '1', '1', '1'), ('602', '101', '101', '1', '写数据表组件', 'write_data_table', 'writeDataTable', '', '当前采用覆盖写入方式，请慎重选择已存在的表(或分区)', '', '4', '{}', '2018-11-09 17:59:50', '2018-11-09 17:59:50', '0', '1', '1', '1', '1'), ('603', '601', '90001', '1', '版本', 'launch_venv', 'select', 'mxnet1.0_py2', '', '', '1', '{\"options\":[{\"value\":\"mxnet1.0_py2\",\"key\":\"V1.0(python2)\"},{\"value\":\"mxnet-gpu1.2.1_py2\",\"key\":\"V1.2.1 for GPU(python2)\"}]}', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '3', '1', '1', '1'), ('604', '601', '90001', '1', 'Python代码文件', '__codeFiles', 'codeUploader', '', '支持.py文件和.tgz、.tar、.zip、.tar.gz后缀的压缩文件', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '3', '1', '1', '1'), ('605', '601', '90001', '1', 'Python主文件', '__launchFile', 'input', '', '如果上传压缩包填写相对压缩包内部目录结构的相对路径', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '3', '1', '1', '1'), ('606', '601', '90001', '1', '超参配置', '__launchArgs', 'keyValue', '', '超参配置将在运行时通过命令行参数传入启动脚本', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '3', '0', '1', '1'), ('607', '601', '90001', '1', '框架类型', 'app-type', 'input', 'mxnet', '表示框架类型，不再页面展示', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '2', '1', '1', '1'), ('608', '601', '90001', '1', '样本数据', 'input_path', 'input', '', '样本数据，端点不再页面展示', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '2', '1', '1', '1'), ('609', '601', '90001', '1', '模型路径', 'output_path', 'input', '', '模型路径，端点不再页面展示', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '2', '1', '1', '1'), ('611', '602', '90001', '1', '版本', 'launch_venv', 'select', 'caffe2-1.0_py2', '', '', '1', '{\"options\":[{\"value\":\"caffe2-1.0_py2\",\"key\":\"V1.0(python2)\"}]}', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '3', '1', '1', '1'), ('612', '602', '90001', '1', 'Python代码文件', '__codeFiles', 'codeUploader', '', '支持.py文件和.tgz、.tar、.zip、.tar.gz后缀的压缩文件', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '3', '1', '1', '1'), ('613', '602', '90001', '1', 'Python主文件', '__launchFile', 'input', '', '如果上传压缩包填写相对压缩包内部目录结构的相对路径', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '3', '1', '1', '1'), ('614', '602', '90001', '1', '超参配置', '__launchArgs', 'keyValue', '', '超参配置将在运行时通过命令行参数传入启动脚本', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '3', '0', '1', '1'), ('615', '602', '90001', '1', '框架类型', 'app-type', 'input', 'caffe2', '表示框架类型，不再页面展示', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '2', '1', '1', '1'), ('616', '602', '90001', '1', '样本数据', 'input_path', 'input', '', '样本数据，端点不再页面展示', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '2', '1', '1', '1'), ('617', '602', '90001', '1', '模型路径', 'output_path', 'input', '', '模型路径，端点不再页面展示', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '2', '1', '1', '1'), ('619', '603', '90001', '1', '版本', 'launch_venv', 'select', 'xgboost0.72_py2', '', '', '1', '{\"options\":[{\"value\":\"xgboost0.72_py2\",\"key\":\"V0.72(python2)\"},{\"value\":\"xgboost-gpu0.72_py2\",\"key\":\"V0.72 for GPU(python2)\"}]}', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '3', '1', '1', '1'), ('620', '603', '90001', '1', 'Python代码文件', '__codeFiles', 'codeUploader', '', '支持.py文件和.tgz、.tar、.zip、.tar.gz后缀的压缩文件', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '3', '1', '1', '1'), ('621', '603', '90001', '1', 'Python主文件', '__launchFile', 'input', '', '如果上传压缩包填写相对压缩包内部目录结构的相对路径', 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'输出路径，端点不再页面展示', '', '1', '', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0', '2', '1', '1', '1'), ('635', '102', '102', '1', '读数据表组件', 'read_data_table', 'readDataTable', '', '必选', '', '3', '{}', '2018-11-09 21:33:10', '2018-11-09 21:33:10', '0', '1', '1', '1', '1'), ('636', '599', '599', '1', '特征列', 'feature_cols', 'selectField', '', '', '', '1', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '1', '3', '1', '4', '1'), ('637', '599', '599', '1', '额外列(例如权重列)', 'append_cols', 'selectField', '', '', '', '2', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '1', '3', '0', '4', '1'), ('638', '599', '599', '1', '结果列', 'result_col', 'input', 'prediction_result', '用于指定预测结果的列名', '', '5', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '0', '3', '1', '1', '1'), ('639', '599', '599', '1', '分数列名', 'score_col', 'input', 'prediction_score', '用于指定预测结果对应得分的列名，对于聚类及分类，分数定义结果类别的指标，对于回归，则与结果一致', '', '6', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '0', '3', '1', '1', '1'), ('640', '599', '599', '1', '详细列名', 'detail_col', 'input', 'prediction_detail', '用于指定在预测详情的列名，对于聚类及分类，详情指分配到每个类别的指标，对于回归，则表示\"label:结果\"', '', '7', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '0', '3', '1', '1', '1'), ('641', '599', '599', '1', '稀疏格式', 'enable_sparse', 'boolean', '', '', 'false', '6', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '1', '3', '0', '1', '1'), ('642', '599', '599', '1', 'key和value的分割符', 'kv_delimiter', 'input', '', ':', '', '7', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '1', '3', '0', '1', '1'), ('643', '599', '599', '1', 'kv间的分割符', 'item_delimiter', 'input', '', '', ' ', '8', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '1', '3', '0', '1', '1'), ('644', '599', '599', '1', '预测结果表', 'output_table', 'output', '', '使用机器学习模型预测后的结果表，其中包含保留列，结果列，分数列及详情列', '', '4', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '0', '2', '1', '1', '1'), ('645', '599', '599', '1', '待预测数据表', 'input_table', 'input', '', '待预测数据表，其中必须包含训练机器学习模型时所使用的特征，要注意特征名称及数据含义的一致性，否则结果无法预测', '', '1', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '0', '2', '1', '1', '1'), ('646', '599', '599', '1', '机器学习模型', 'model_name', 'input', '', '使用指定机器学习模型组件生成的机器学习模型', '', '2', '{}', '2018-11-10 14:52:21', '2018-11-10 14:52:21', '0', '2', '1', '1', '1'), ('648', '502', '502', '1', '权重列', 'weight_col', 'selectField', '', '训练表中的权重列', '训练表中的权重列', '4', '{}', '2018-11-12 14:13:03', '2018-11-12 14:13:03', '0', '3', '0', '4', '1'), ('649', '515', '515', '1', '预测结果表', 'input_table', 'input', '', '经过二分类模型预测的输出表', '经过二分类模型预测的输出表', '1', '{}', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0', '2', '1', '1', '1'), ('651', '515', '515', '1', '真实结果列', 'label_col', 'selectField', '', '样本的真实结果列，其值只能有两种', '样本的真实结果列，其值只能有两种', '2', '{}', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0', '3', '1', '4', '1'), ('652', '515', '515', '1', '预测结果列', 'score_col', 'selectField', '', '为预测组件输出的预测结果，其值只能有两种', '为预测组件输出的预测结果，其值只能有两种', '3', '{}', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0', '3', '1', '4', '1'), ('653', '515', '515', '1', '分组列', 'group_col', 'selectField', '', '用于分组评估的场景，该列不能与真实结果列及预测结果列相同', '用于分组评估的场景，该列不能与真实结果列及预测结果列相同', '4', '{}', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0', '3', '0', '4', '1'), ('654', '515', '515', '1', '分箱数', 'bin_count', 'input', '1000', '计算KS、PR等指标时按等频分箱的个数', '计算KS、PR等指标时按等频分箱的个数', '5', '{}', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0', '3', '0', '1', '1'), ('655', '515', '515', '1', '指标表', 'output_metric_table', 'input', '', '输出评估指标，包括指标名和指标列', '输出评估指标，包括指标名和指标列', '6', '{}', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0', '2', '1', '1', '1'), ('656', '515', '515', '1', '详细表', 'output_detail_table', 'input', '', '输出用于绘制评估报告的详细数据表', '输出用于绘制评估报告的详细数据表', '7', '{}', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0', '2', '0', '1', '1'), ('657', '515', '515', '1', '正样本标签', 'positive_label', 'input', '1', '正样本的分类标签', '正样本的分类标签', '8', '{}', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0', '3', '0', '1', '1'), ('659', '101', '101', '1', '分区', 'open_partition', 'boolean', 'false', '可选', '', '5', '{}', '2018-11-12 16:50:28', '2018-11-12 16:50:28', '0', '2', '0', '1', '1'), ('660', '102', '102', '1', '分区', 'open_partition', 'boolean', 'false', '可选', '', '3', '{}', '2018-11-12 16:51:20', '2018-11-12 16:51:20', '0', '2', '0', '1', '1'), ('661', '713', '713', '1', '输入表名', 'input_table', 'input', '', '表名', '', '1', '{}', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '2', '1', '1', '1'), ('662', '713', '713', '1', '输出表名', 'output_table', 'input', '', '表名', '', '1', '{}', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '2', '1', '1', '1'), ('663', '713', '713', '1', '相似度计算中第一列的列名', 'input_selected_col1', 'selectField', '', '可选', '表中第一个为类型为string的列名', '1', '{}', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '3', '0', '4', '1'), ('664', '713', '713', '1', '相似度计算中第二列的列名', 'input_selected_col2', 'selectField', '', '可选', '表中第二个为类型为string的列名', '2', '{}', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '3', '0', '4', '1'), ('665', '713', '713', '1', '输出表追加的列名', 'input_append_cols', 'selectField', '', '可选', '', '1', '{}', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '3', '0', '4', '1'), ('666', '713', '713', '1', '输入表选中的分区', 'input_partitions', 'selectField', '', '可选', '', '2', '{}', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '2', '0', '4', '1'), ('667', '713', '713', '1', '输出表中相似度列的列名', 'output_col', 'input', 'output', '可选', '列名中不能有特殊字符', '2', '{}', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '3', '0', '1', '1'), ('668', '713', '713', '1', '相似度计算方法', 'method', 'select', 'levenshtein_sim', '可选', '', '2', '{\"options\":[{\"key\":\"levenshtein\",\"value\":\"levenshtein\"},{\"key\":\"levenshtein_sim\",\"value\":\"levenshtein_sim\"},{\"key\":\"lcs\",\"value\":\"lcs\"},{\"key\":\"lcs_sim\",\"value\":\"lcs_sim\"},{\"key\":\"ssk\",\"value\":\"ssk\"},{\"key\":\"cosine\",\"value\":\"cosine\"},{\"key\":\"simhash_hamming\",\"value\":\"simhash_hamming\"},{\"key\":\"simhash_hamming_sim\",\"value\":\"simhash_hamming_sim\"},]}', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '3', '0', '1', '1'), ('669', '713', '713', '1', '匹配字符串的权重', 'lambdaqz', 'input', '0.5', '可选', '', '2', '{\"type\":\"float\",\"min\":0,\"max\":1}', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '3', '0', '1', '1'), ('670', '713', '713', '1', '子串的长度', 'k', 'input', '2', '可选', '', '2', '{\"type\":\"float\",\"min\":0,\"max\":100}', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0', '3', '0', '1', '1'), ('672', '714', '714', '1', '输入表名', 'input_table_name', 'input', '', '必选', '', '1', '{}', '2018-11-12 17:15:01', '2018-11-12 17:15:01', '0', '2', '1', '1', '1'), ('673', '714', '714', '1', '输出表名', 'output_table_name', 'input', '', '必选', '', '1', '{}', '2018-11-12 17:15:01', '2018-11-12 17:15:01', '0', '2', '1', '1', '1'), ('674', '714', '714', '1', '标识文章id的列名', 'doc_id_col', 'selectField', '', '必选', '', '1', '{}', '2018-11-12 17:15:01', '2018-11-12 17:15:01', '0', '3', '1', '4', '1'), ('675', '714', '714', '1', '标示文章内容的列名', 'doc_content', 'selectField', '', '必选', '', '1', '{}', '2018-11-12 17:15:01', '2018-11-12 17:15:01', '0', '3', '1', '4', '1'), ('676', '714', '714', '1', '句子的间隔字符集合', 'delimiter', 'input', '。|！|？', '可选', '', '1', '{}', '2018-11-12 17:15:01', '2018-11-12 17:15:01', '0', '3', '0', '1', '1'), ('677', '714', '714', '1', '输出id的列名', 'output_doc_id_name', 'input', 'doc_id', '可选', '', '1', '{}', '2018-11-12 17:15:01', '2018-11-12 17:15:01', '0', '3', '0', '1', '1'), ('678', '714', '714', '1', '输出句子的列名', 'output_sentence_name', 'input', 'sentence', '可选', '', '1', '{}', '2018-11-12 17:15:01', '2018-11-12 17:15:01', '0', '3', '0', '1', '1'), ('680', '80', '80', '1', '模型名称', 'output_table', 'select', '', '', '', '1', '{\"optionsUrl\":\"bare_model/get_model\"}', '2018-11-12 17:58:51', '2018-11-12 17:58:51', '0', '3', '1', '1', '1'), ('681', '707', '707', '1', '输入表名', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-12 19:12:04', '2018-11-12 19:12:04', '0', '2', '1', '1', '1'), ('682', '707', '707', '1', '输出词表', 'output_table', 'input', '', '', '', '1', '{}', '2018-11-12 19:12:04', '2018-11-12 19:12:04', '0', '2', '1', '1', '1'), ('683', '707', '707', '1', '输入表选择列,以逗号分隔', 'input_col_name', 'selectField', '', '', '', '1', '{}', '2018-11-12 19:12:04', '2018-11-12 19:12:04', '0', '3', '0', '4', '1'), ('684', '707', '707', '1', 'N-grams的最大长度', 'order', 'intInput', '3', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-12 19:12:04', '2018-11-12 19:12:04', '0', '3', '0', '1', '1'), ('685', '600', '90000', '2', 'PS个数', 'ps_num', 'intInput', '0', '参数服务器个数，ps个数大于表示分布式模式', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('686', '600', '90000', '2', 'PS CPU核数', 'ps_cores', 'intInput', '0', '每个参数服务器cpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('687', '600', '90000', '2', 'PS内存', 'ps_memory', 'input', '0', '每个参数服务器内存大小，支持单位(G、M、K)', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('688', '600', '90000', '2', 'Worker个数', 'worker_num', 'intInput', '1', 'worker服务器个数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('689', '600', '90000', '2', 'Worker CPU核数', 'worker_cores', 'intInput', '1', '每个worker服务器cpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('690', '600', '90000', '2', 'Worker GPU核数', 'worker_gcores', 'intInput', '0', '每个worker服务器gpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('691', '600', '90000', '2', 'Worker内存', 'worker_memory', 'input', '2G', '每个worker服务器内存大小，支持单位(G、M、K)', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('692', '207', '207', '1', '数据表', 'input_table', 'input', '', '需要进行奇异值分解的数据表', '', '1', '{}', '2018-11-13 15:25:03', '2018-11-13 15:25:03', '0', '2', '1', '1', '1'), ('693', '207', '207', '1', '特征列', 'selected_cols', 'selectField', '', '需要进行奇异值分解的特征列', '', '1', '{\"limitSize\":1}', '2018-11-13 15:25:03', '2018-11-13 15:25:03', '0', '3', '1', '4', '1');
INSERT INTO `bas_node_config` VALUES ('694', '207', '207', '1', '左特征向量表', 'output_u_table', 'input', '', '左特征向量表', '', '1', '{}', '2018-11-13 15:25:03', '2018-11-13 15:25:03', '0', '2', '1', '1', '1'), ('695', '207', '207', '1', '奇异值表', 'output_s_table', 'input', '', '奇异值表', '', '1', '{}', '2018-11-13 15:25:03', '2018-11-13 15:25:03', '0', '2', '1', '1', '1'), ('696', '207', '207', '1', '右特征向量表', 'output_v_table', 'input', '', '右特征向量表', '', '1', '{}', '2018-11-13 15:25:03', '2018-11-13 15:25:03', '0', '2', '1', '1', '1'), ('697', '207', '207', '1', '奇异值数量', 'k', 'intInput', '', 'SVD分解后需要保留的奇异值个数，需要小于特征个数与数据行数的最大值	', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-13 15:25:03', '2018-11-13 15:25:03', '0', '3', '1', '1', '1'), ('698', '207', '207', '1', '收敛精度', 'tol', 'floatInput', '1.00E-06', '算法最终的收敛精度。若两次迭代之间的误差小于该值，则停止迭代', '', '1', '{\"type\":\"float\",\"min\":0}', '2018-11-13 15:25:03', '2018-11-13 15:25:03', '0', '3', '0', '1', '1'), ('699', '207', '207', '1', '特征是否稀疏', 'enable_sparse', 'boolean', 'false', '特征列是否为稀疏存储', '', '1', '{}', '2018-11-13 15:25:03', '2018-11-13 15:25:03', '0', '3', '0', '1', '1'), ('700', '207', '207', '1', '项分隔符', 'item_delimiter', 'input', ' ', '键值对与键值对之间的分隔符。若特征为稀疏存储，才可以对其进行设置', '', '1', '{}', '2018-11-13 15:25:03', '2018-11-13 15:25:03', '0', '3', '0', '1', '1'), ('701', '207', '207', '1', '键值对分隔符', 'kv_delimiter', 'input', ':', '键与值之间的分隔符。若特征为稀疏存储，才可以对其进行设置', '', '1', '{}', '2018-11-13 15:25:03', '2018-11-13 15:25:03', '0', '3', '0', '1', '1'), ('702', '710', '710', '7', '输入表名', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-13 15:26:58', '2018-11-13 15:26:58', '0', '2', '1', '1', '1'), ('703', '710', '710', '6', '需要计算相近向量的id的列表所在表名', 'id_table', 'input', '', '', '', '1', '{}', '2018-11-13 15:26:58', '2018-11-13 15:26:58', '1', '2', '1', '1', '1'), ('704', '710', '710', '5', '输出表名', 'output_table', 'input', '', '', '', '1', '{}', '2018-11-13 15:26:58', '2018-11-13 15:26:58', '0', '2', '1', '1', '1'), ('705', '710', '710', '1', 'id所在列名', 'id_col', 'selectField', '', '', '', '1', '{\"limitSize\":1}', '2018-11-13 15:26:58', '2018-11-13 15:26:58', '0', '3', '1', '4', '1'), ('706', '710', '710', '2', '向量的列名列表', 'vector_cols', 'selectField', '', '', '', '1', '{}', '2018-11-13 15:26:58', '2018-11-13 15:26:58', '0', '3', '0', '4', '1'), ('707', '710', '710', '3', '输出的距离最近的向量的数目', 'top_n', 'intInput', '5', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-13 15:26:58', '2018-11-13 15:26:58', '0', '3', '0', '1', '1'), ('708', '710', '710', '4', '距离的计算方式', 'distance_type', 'select', 'euclidean', '', '', '1', '{\"options\":[{\"key\":\"euclidean\",\"value\":\"euclidean\"},{\"key\":\"cosine\",\"value\":\"cosine\"},{\"key\":\"manhattan\",\"value\":\"manhattan\"}]}', '2018-11-13 15:26:58', '2018-11-13 15:26:58', '0', '3', '0', '1', '1'), ('709', '800', '800', '1', '训练表', 'input_train_table', 'input', '', '训练数据表，必须包括特征列及标签列', '训练数据表，必须包括特征列及标签列', '1', '{}', '2018-11-13 15:48:47', '2018-11-13 15:48:47', '0', '2', '1', '1', '1'), ('710', '800', '800', '1', '测试表', 'input_test_table', 'input', '', '测试或预测数据表，至少包含特征列，并且特征名称与训练表中完全一致', '测试或预测数据表，至少包含特征列，并且特征名称与训练表中完全一致', '2', '{}', '2018-11-13 15:48:47', '2018-11-13 15:48:47', '0', '2', '1', '1', '1'), ('711', '800', '800', '1', '输出表', 'output_table', 'input', '', '测试或预测数据在使用K近邻算法后的预测结果表', '测试或预测数据在使用K近邻算法后的预测结果表', '3', '{}', '2018-11-13 15:48:47', '2018-11-13 15:48:47', '0', '2', '1', '1', '1'), ('712', '800', '800', '1', '特征列', 'selected_cols', 'selectField', '', '训练表及测试表参与计算特征和列名', '训练表及测试表参与计算特征和列名', '4', '{}', '2018-11-13 15:48:47', '2018-11-13 15:48:47', '0', '3', '1', '4', '1'), ('713', '800', '800', '1', '标签列', 'label_col', 'selectField', '', '训练表中的标签列', '训练表中的标签列', '5', '{\"limitSize\":1}', '2018-11-13 15:48:47', '2018-11-13 15:48:47', '0', '3', '1', '4', '1'), ('714', '800', '800', '1', '保留列', 'remain_cols', 'selectField', '', '测试或预测的结果表中所需保留测试或预测数据的列名', '测试或预测的结果表中所需保留测试或预测数据的列名', '6', '{}', '2018-11-13 15:48:47', '2018-11-13 15:48:47', '0', '3', '1', '4', '1'), ('715', '800', '800', '1', '最近邻个数', 'k', 'intInput', '5', '最近邻个数，算法会按选择最近的k个进行计算', '最近邻个数，算法会按选择最近的k个进行计算', '7', '{\"type\":\"int\",\"min\":1,\"max\":1000}', '2018-11-13 15:48:47', '2018-11-13 15:48:47', '0', '3', '0', '1', '1'), ('716', '800', '800', '1', '特征是否稀疏', 'enable_sparse', 'boolean', 'false', '特征列是否为稀疏存储', '特征列是否为稀疏存储', '8', '{}', '2018-11-13 15:48:47', '2018-11-13 15:48:47', '0', '3', '0', '1', '1'), ('717', '800', '800', '1', '项分隔符', 'item_delimiter', 'input', ' ', '键值对与键值对之间的分隔符。若特征为稀疏存储，才可以对其进行设置', '键值对与键值对之间的分隔符。若特征为稀疏存储，才可以对其进行设置', '9', '{}', '2018-11-13 15:48:47', '2018-11-13 15:48:47', '0', '3', '0', '1', '1'), ('718', '800', '800', '1', '键值对分隔符', 'kv_delimiter', 'input', ':', '键与值之间的分隔符。若特征为稀疏存储，才可以对其进行设置', '键与值之间的分隔符。若特征为稀疏存储，才可以对其进行设置', '10', '{}', '2018-11-13 15:48:47', '2018-11-13 15:48:47', '0', '3', '0', '1', '1'), ('719', '708', '708', '1', '需要保留的列', 'remain_cols', 'selectField', '', '可选', '', '1', '{}', '2018-11-13 16:44:07', '2018-11-13 16:44:07', '0', '3', '1', '4', '1'), ('720', '511', '511', '1', '训练表', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '', '1', '{}', '2018-11-13 17:12:47', '2018-11-13 17:12:47', '0', '2', '1', '1', '1'), ('721', '511', '511', '1', '特征列', 'selected_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '1', '{}', '2018-11-13 17:12:47', '2018-11-13 17:12:47', '0', '3', '0', '4', '1'), ('722', '511', '511', '1', '聚类数', 'center_count', 'input', '', '训练数据需要被聚类的类别个数', '正整数', '7', '{\"type\":\"int\",\"min\":1,\"max\":1000}', '2018-11-13 17:12:47', '2018-11-13 17:12:47', '0', '3', '1', '1', '1'), ('723', '511', '511', '1', '模型路径', 'model_name', 'input', '', '模型训练完成后在HDFS上的存储路径', '模型名', '6', '{}', '2018-11-13 17:12:47', '2018-11-13 17:12:47', '0', '2', '1', '1', '1'), ('724', '511', '511', '1', '迭代次数', 'loop', 'input', '20', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '', '8', '{\"type\":\"int\",\"min\":1,\"max\":1000}', '2018-11-13 17:12:47', '2018-11-13 17:12:47', '0', 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'1'), ('735', '700', '700', '1', '文章内容列', 'doc_content', 'selectField', '', '', '', '1', '{\"limitSize\":1}', '2018-11-13 17:57:22', '2018-11-13 17:57:22', '0', '3', '1', '4', '1'), ('736', '700', '700', '1', '输入表', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-13 17:57:22', '2018-11-13 17:57:22', '0', '2', '1', '1', '1'), ('737', '700', '700', '1', '输出保序词语表', 'output_table_name_multi', 'input', '', '', '', '1', '{}', '2018-11-13 17:57:22', '2018-11-13 17:57:22', '0', '2', '0', '1', '1'), ('738', '700', '700', '1', '输出词频统计表', 'output_table_name_triple', 'input', '', '', '', '1', '{}', '2018-11-13 17:57:22', '2018-11-13 17:57:22', '0', '2', '0', '1', '1'), ('739', '703', '703', '1', '输入表名', 'input_table', 'input', '', '', '', '1', '{}', '2018-11-13 18:05:53', '2018-11-13 18:05:53', '0', '2', '1', '1', '1'), ('740', '703', '703', '1', 'P(z/d)输出表', 'pzd_table', 'input', '', '', '', '1', '{}', '2018-11-13 18:05:53', '2018-11-13 18:05:53', '0', '2', '1', '1', '1'), ('741', '703', '703', '1', 'P(w/z)输出表', 'pwz_table', 'input', '', '', '', '1', '{}', '2018-11-13 18:05:53', '2018-11-13 18:05:53', '0', '2', '1', '1', '1'), ('742', '703', '703', '1', '输入表中用于LDA的列名,目前只支持kv格式的输入', 'selected_cols', 'selectField', '', '', '', '1', '{\"limitSize\":1}', '2018-11-13 18:05:53', '2018-11-13 18:05:53', '0', '3', '0', '4', '1'), ('743', '703', '703', '1', 'kv与kv间的分隔符', 'item_delimiter', 'input', ' ', '', '', '1', '{}', '2018-11-13 18:05:53', '2018-11-13 18:05:53', '0', '3', '0', '1', '1'), ('744', '703', '703', '1', 'key和value间的分隔符', 'kv_delimiter', 'input', ':', '', '', '1', '{}', '2018-11-13 18:05:53', '2018-11-13 18:05:53', '0', '3', '0', '1', '1'), ('745', '703', '703', '1', 'P(z/d)的先验狄利克雷分布', 'alpha', 'floatInput', '1.0', 'P(z/d)的先验狄利克雷分布的参数，必须>1.0', '', '1', '{\"type\":\"float\"}', '2018-11-13 18:05:53', '2018-11-13 18:05:53', '0', '3', '0', '1', '1'), ('746', '703', '703', '1', 'P(w/z)的先验狄利克雷分布', 'beta', 'floatInput', '1.0', 'P(w/z)的先验狄利克雷分布的参数，必须>1.0', '', '1', '{\"type\":\"float\"}', '2018-11-13 18:05:53', '2018-11-13 18:05:53', '0', '3', '0', '1', '1'), ('747', '703', '703', '1', '模型训练的迭代次数', 'total_iter', 'intInput', '1', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-13 18:05:53', '2018-11-13 18:05:53', '0', '3', '0', '1', '1'), ('748', '703', '703', '1', 'topic的数量', 'topic_num', 'intInput', '', '', '', '1', '{\"type\":\"int\",\"min\":1}', '2018-11-13 18:05:53', '2018-11-13 18:05:53', '0', '3', '1', '1', '1'), ('749', '506', '506', '1', '	训练表', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '训练数据表，必须包括特征列及标签列', '1', '{}', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0', '2', '1', '1', '1'), ('750', '506', '506', '1', '模型路径', 'model_name', 'input', '', '模型训练完成后在HDFS上的存储路径', '模型训练完成后在HDFS上的存储路径', '5', '{}', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0', '2', '1', '1', '1'), ('751', '506', '506', '1', '标签列	', 'label_col', 'selectField', '', '训练表中的标签列', '训练表中的标签列', '3', '{}', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0', '3', '1', '4', '1'), ('752', '506', '506', '1', '特征列', 'feature_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '2', '{}', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0', '3', '1', '4', '1'), ('753', '506', '506', '1', '迭代次数', 'max_iter', 'input', '100', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '7', '{}', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0', '3', '0', '1', '1'), ('754', '506', '506', '1', '控制鲁棒性的参数', 'epsilon', 'input', '1.35', '只有当损失函数为huber时才有效', '只有当损失函数为huber时才有效', '13', '{\"type\":\"float\",\"min\":1.000000001}', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0', '3', '0', '1', '1'), ('755', '506', '506', '1', '正则化参数', 'reg_param', 'input', '0', '使用L2正则化时的正则化参数，若为0，则不使用正则化', '使用L2正则化时的正则化参数，若为0，则不使用正则化', '8', '{\"type\":\"int\",\"min\":0}', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0', '3', '0', '1', '1'), ('756', '506', '506', '1', '最优化方法', 'solver', 'input', 'auto', '求解最优化问题的方法', '求解最优化问题的方法', '11', '{\"options\":[{\"key\":\"auto\",\"value\":\"auto\"},{\"key\":\"normal\",\"value\":\"normal\"},{\"key\":\"l-bfgs\",\"value\":\"l-bfgs\"}]}', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0', '3', '0', '1', '1'), ('757', '715', '715', '1', '表名', 'input_table', 'input', '', '必选', '', '1', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '2', '1', '1', '1'), ('758', '715', '715', '1', '转成kv表时保持不变的列名', 'id_col', 'selectField', '', '必选', '', '2', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '3', '1', '4', '1'), ('759', '715', '715', '1', 'kv中的key', 'key_col', 'selectField', '', '必选', '', '3', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '3', '1', '4', '1'), ('760', '715', '715', '1', 'kv中的value', 'value_col', 'selectField', '', '必选', '', '4', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '3', '1', '4', '1'), ('761', '715', '715', '1', '输出key的索引表', 'index_output_table', 'input', '', '必选', '', '5', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '2', '1', '1', '1'), ('762', '715', '715', '1', '输出kv表名', 'output_table', 'input', '', '必选', '', '9', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '2', '1', '1', '1'), ('763', '715', '715', '1', '输出kv表的kv列名', 'output_kv_col', 'input', 'key_value', '可选', '', '10', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '3', '0', '1', '1'), ('764', '715', '715', '1', '输出索引表的索引列名', 'index_output_id_col', 'input', 'index', '可选', '', '11', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '3', '0', '1', '1'), ('765', '715', '715', '1', '输入已有的索引表', 'index_input_table', 'input', '', '可选', '', '12', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '2', '0', '1', '1'), ('766', '715', '715', '1', '输入索引表key的列名', 'index_input_key_col', 'selectField', '', '可选', '', '7', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '3', '0', '4', '1'), ('767', '715', '715', '1', '输入索引表key索引号的列名', 'index_input_key_id_col', 'selectField', '', '可选', '', '8', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '3', '0', '4', '1'), ('768', '715', '715', '1', '输入表的分区', 'input_partitions', 'input', '', '可选', '', '15', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '2', '0', '1', '1'), ('769', '715', '715', '1', '当输入表数据为稀疏格式时,kv间的分割', 'item_delimiter', 'input', ',', '', '', '13', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '3', '0', '1', '1'), ('770', '715', '715', '1', '当输入表数据为稀疏格式时,key和value的分割符', 'kv_delimiter', 'input', ':', '', '', '14', '{}', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0', '3', '0', '1', '1'), ('787', '809', '809', '1', '训练表', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '训练数据表，必须包括特征列及标签列', '1', '{}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '2', '1', '1', '1'), ('788', '809', '809', '1', '模型路径', 'model_name', 'input', '', '模型训练完成后在HDFS上的存储路径', '模型训练完成后在HDFS上的存储路径', '7', '{}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '2', '1', '1', '1'), ('789', '809', '809', '1', '特征列', 'selected_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '2', '{}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '1', '4', '1'), ('790', '809', '809', '1', '标签列', 'label_col', 'selectField', '', '训练表中的标签列', '训练表中的标签列', '3', '{\"limitSize\":1}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '1', '4', '1'), ('791', '809', '809', '1', '迭代次数', 'max_iter', 'intInput', '100', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '5', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('792', '809', '809', '1', '树深度', 'max_depth', 'intInput', '5', '决策树的最大深度，可以通过该参数控制模型的复杂度', '决策树的最大深度，可以通过该参数控制模型的复杂度', '6', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('793', '809', '809', '1', '随机种子', 'seed', 'intInput', '0', '生成随机数的随机种子', '生成随机数的随机种子', '13', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('794', '809', '809', '1', '子节点最小个数', 'mininstances_pernode', 'intInput', '1', '节点分割后子节点的最小个数，若分割后小于该数量，则取消分割', '节点分割后子节点的最小个数，若分割后小于该数量，则取消分割', '11', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('795', '809', '809', '1', '分割最小信息增益', 'mininfo_gain', 'floatInput', '0.0', '树节点分割时的最小信息增益', '树节点分割时的最小信息增益', '8', '{\"type\":\"float\",\"min\":0.0}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('796', '809', '809', '1', '聚合时的最大内存', 'maxmemory_inmb', 'intInput', '256', '数据聚合时所分配的最大内存，单位为MB，如果值太小，则聚合时可能出错', '数据聚合时所分配的最大内存，单位为MB，如果值太小，则聚合时可能出错', '9', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('797', '809', '809', '1', '是否缓存实例ID', 'cachenode_ids', 'boolean', 'False', '如果是，则对每一个实例都缓存node ID, 加速训练。可以通过checkpoint_interval配置多久缓存一次，或者禁用', '如果是，则对每一个实例都缓存node ID, 加速训练。可以通过checkpoint_interval配置多久缓存一次，或者禁用', '10', '{}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('798', '809', '809', '1', '检查点间隔', 'checkpoint_interval', 'intInput', '10', '设置检查间隔(>=1)或禁用(-1)；例如，设置为10，则每10次迭代会缓存一次配置', '设置检查间隔(>=1)或禁用(-1)；例如，设置为10，则每10次迭代会缓存一次配置', '12', '{\"type\":\"int\",\"min\":-1}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('799', '809', '809', '1', '分箱数', 'max_bins', 'intInput', '32', '离散连续特征时的最大分箱数，需要大于等于2并且大于等于离散特征的分类数', '离散连续特征时的最大分箱数，需要大于等于2并且大于等于离散特征的分类数', '14', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('800', '809', '809', '1', '数据采样比例', 'subsampling_rate', 'floatInput', '1.0', '训练每个决策树的数据占总训练数据的比例', '训练每个决策树的数据占总训练数据的比例', '15', '{\"type\":\"float\",\"min\":0.0,\"max\":1.0}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('801', '809', '809', '1', '学习步长', 'step_size', 'floatInput', '0.1', '学习步长', '学习步长', '7', '{\"type\":\"int\",\"min\":0,\"max\":1}', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0', '3', '0', '1', '1'), ('802', '810', '810', '1', '训练表', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '', '1', '{}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '2', '1', '1', '1'), ('803', '810', '810', '1', '模型路径', 'model_name', 'input', '', '模型训练完成后在HDFS上的存储路径', '', '7', '{}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '2', '1', '1', '1'), ('804', '810', '810', '1', '聚类结果表', 'idx_table', 'input', '', '包含聚类类别的聚类结果表', '', '4', '{}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '2', '1', '1', '1'), ('805', '810', '810', '1', '聚类统计表', 'cluster_count_table', 'input', '', '包含每个类别样本数量的统计表', '', '5', '{}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '2', '1', '1', '1'), ('806', '810', '810', '1', '高斯分布表', 'distribut_table', 'input', '', '包含每个类别样本数量的统计表', '', '6', '{}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '2', '1', '1', '1'), ('807', '810', '810', '1', '特征列', 'selected_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '2', '{}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '3', '0', '4', '1'), ('808', '810', '810', '1', '保留列', 'append_cols', 'selectField', '', '训练表中需要在聚类结果表中输出的列', '', '3', '{}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '3', '0', '4', '1'), ('809', '810', '810', '1', '聚类数', 'k', 'intInput', '', '训练数据需要被聚类的类别个数', '', '8', '{\"type\":\"int\",\"min\":1,\"max\":1000}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '3', '1', '1', '1'), ('810', '810', '810', '1', '收敛精度', 'tol', 'floatInput', '0.0001', '算法最终的收敛精度。若两次迭代之间的误差小于该值，则停止迭代', '', '10', '{\"type\":\"float\",\"min\":0.0,\"max\":1.0}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '3', '0', '1', '1'), ('811', '810', '810', '1', '迭代次数', 'max_iter', 'intInput', '10', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '', '9', '{\"type\":\"int\",\"min\":1,\"max\":1000}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '3', '0', '1', '1'), ('812', '810', '810', '1', '随机种子', 'seed', 'intInput', '', '生成随机数的随机种子', '', '11', '{\"type\":\"int\",\"min\":0}', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0', '3', '0', '1', '1'), ('841', '808', '808', '1', '训练表', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '训练数据表，必须包括特征列及标签列', '1', '{}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '2', '1', '1', '1'), ('842', '808', '808', '1', '模型路径', 'model_name', 'input', '', '模型训练完成后在HDFS上的存储路径', '模型训练完成后在HDFS上的存储路径', '4', '{}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '2', '1', '1', '1'), ('843', '808', '808', '1', '特征列', 'selected_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '2', '{}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '1', '4', '1'), ('844', '808', '808', '1', '标签列', 'label_col', 'selectField', '', '训练表中的标签列', '训练表中的标签列', '3', '{\"limitSize\":1}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '1', '4', '1'), ('845', '808', '808', '1', '树深度', 'max_depth', 'intInput', '5', '决策树的最大深度，可以通过该参数控制模型的复杂度', '决策树的最大深度，可以通过该参数控制模型的复杂度', '7', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('846', '808', '808', '1', '树个数', 'num_trees', 'intInput', '20', '随机森林算法中待训练的树的数量', '随机森林算法中待训练的树的数量', '5', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('847', '808', '808', '1', '随机种子', 'seed', 'intInput', '', '生成随机数的随机种子', '生成随机数的随机种子', '13', '{\"type\":\"int\",\"min\":0}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('848', '808', '808', '1', '子节点最小个数', 'mininstances_pernode', 'intInput', '1', '节点分割后子节点的最小个数，若分割后小于该数量，则取消分割', '节点分割后子节点的最小个数，若分割后小于该数量，则取消分割', '11', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('849', '808', '808', '1', '分割最小信息增益', 'mininfo_gain', 'floatInput', '0.0', '树节点分割时的最小信息增益', '树节点分割时的最小信息增益', '8', '{}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('850', '808', '808', '1', '聚合时的最大内存', 'maxmemory_inmb', 'intInput', '256', '数据聚合时所分配的最大内存，单位为MB，如果值太小，则聚合时可能出错', '数据聚合时所分配的最大内存，单位为MB，如果值太小，则聚合时可能出错', '9', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('851', '808', '808', '1', '是否缓存实例ID', 'cachenode_ids', 'boolean', 'False', '如果是，则对每一个实例都缓存node ID, 加速训练。\r\n\r\n可以通过checkpoint_interval配置多久缓存一次，或者禁用', '如果是，则对每一个实例都缓存node ID, 加速训练。\r\n\r\n可以通过checkpoint_interval配置多久缓存一次，或者禁用', '10', '{}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('852', '808', '808', '1', '信息增益计算方法', 'impurity', 'select', 'gini', '用于计算信息增益的方法', '用于计算信息增益的方法', '6', '{\"options\":[{\"key\":\"entropy\",\"value\":\"entropy\"},{\"key\":\"gini\",\"value\":\"gini\"}]}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('853', '808', '808', '1', '特征采样策略', 'featuresubsets_trategy', 'select', 'auto', '分割节点时，考虑特征的数量。设为auto时，若num_trees==1，则设为all,若num_trees>1,则设为sqrt；', '分割节点时，考虑特征的数量。设为auto时，若num_trees==1，则设为all,若num_trees>1,则设为sqrt；\r\n\r\nall：使用所有特征；onethird：使用1/3特征；sqrt:使用sqrt(#features)；log2：使用log2(#features);', '15', '{\"options\":[{\"key\":\"auto\",\"value\":\"auto\"},{\"key\":\"all\",\"value\":\"all\"},{\"key\":\"sqrt\",\"value\":\"sqrt\"},{\"key\":\"log2\",\"value\":\"log2\"}]}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('854', '808', '808', '1', '数据采样比例', 'subsampling_rate', 'floatInput', '1.0', '训练每个决策树的数据占总训练数据的比例', '训练每个决策树的数据占总训练数据的比例', '16', '{\"type\":\"float\",\"max\":1.0,\"min\":0.0}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('855', '808', '808', '1', '分箱数', 'max_bins', 'intInput', '32', '离散连续特征时的最大分箱数，需要大于等于2并且大于等于离散特征的分类数', '离散连续特征时的最大分箱数，需要大于等于2并且大于等于离散特征的分类数', '14', '{\"type\":\"int\",\"min\":1}', '2018-11-16 20:36:32', '2018-11-16 20:36:32', '0', '3', '0', '1', '1'), ('869', '816', '816', '1', '训练表', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '训练数据表，必须包括特征列及标签列', '1', '{}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '2', '1', '1', '1'), ('870', '816', '816', '1', '特征列', 'selected_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '2', '{}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '3', '1', '4', '1'), ('871', '816', '816', '1', '标签列', 'label_col', 'selectField', '', '训练表中的标签列', '训练表中的标签列', '3', '{\"limitSize\":1}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '3', '1', '4', '1'), ('872', '816', '816', '1', '模型路径', 'model_name', 'input', '', '模型训练完成后在HDFS上的存储路径', '模型训练完成后在HDFS上的存储路径', '4', '{}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '2', '1', '1', '1'), ('873', '816', '816', '1', '权重列', 'weight_col', 'selectField', '', '样本对应的权重。若不设置，则认为所有样本权重相同', '样本对应的权重。若不设置，则认为所有样本权重相同', '5', '{\"limitSize\":1}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '3', '0', '4', '1'), ('874', '816', '816', '1', '迭代次数', 'max_iter', 'intInput', '10', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '6', '{\"type\":\"int\",\"min\":0}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '3', '0', '1', '1'), ('875', '816', '816', '1', '正则化参数', 'reg_param', 'floatInput', '0.0', '使用L2正则化时的正则化参数，若为0，则不使用正则化', '使用L2正则化时的正则化参数，若为0，则不使用正则化', '7', '{\"type\":\"float\",\"min\":0.0}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '3', '1', '1', '1'), ('876', '816', '816', '1', '二分类阀值', 'threshold', 'floatInput', '0.0', '用于判断二分类时所使用的阈值，大于和小于该值分别为一类', '用于判断二分类时所使用的阈值，大于和小于该值分别为一类', '8', '{\"type\":\"float\"}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '3', '0', '1', '1'), ('877', '816', '816', '1', '聚合深度', 'aggregation_depth', 'intInput', '2', '数据聚合操作时，可以分多次聚合，避免因一次聚合将同一组数据加载到一个节点', '数据聚合操作时，可以分多次聚合，避免因一次聚合将同一组数据加载到一个节点', '9', '{\"type\":\"int\",\"min\":2}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '3', '0', '1', '1'), ('878', '816', '816', '1', '收敛精度', 'epsilon', 'floatInput', '0.000001', '算法最终的收敛精度。若两次迭代之间的误差小于该值，则停止迭代', '算法最终的收敛精度。若两次迭代之间的误差小于该值，则停止迭代', '10', '{\"type\":\"float\",\"min\":0,\"max\":1}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '3', '0', '1', '1'), ('879', '816', '816', '1', '是否拟合截距', 'fit_intercept', 'boolean', 'True', '算法是否需要拟合线性模型中的截距项', '算法是否需要拟合线性模型中的截距项', '11', '{}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '3', '0', '4', '1'), ('880', '816', '816', '1', '是否标准化', 'standardization	', 'boolean', 'True', '在训练模型前是否对训练特征进行标准化', '在训练模型前是否对训练特征进行标准化', '12', '{}', '2018-11-16 21:01:30', '2018-11-16 21:01:30', '0', '3', '0', '4', '1'), ('881', '815', '815', '1', '训练表', 'input_table', 'input', '', '训练数据表，必须包括特征列及标签列', '训练数据表，必须包括特征列及标签列', '1', '{}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '2', '1', '1', '1'), ('882', '815', '815', '1', '特征列', 'selected_cols', 'selectField', '', '训练表及预测表参与计算的特征列名', '训练表及预测表参与计算的特征列名', '2', '{}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '1', '4', '1'), ('883', '815', '815', '1', '标签列', 'label_col', 'selectField', '', '训练表中的标签列', '训练表中的标签列', '3', '{\"limitSize\":1}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '1', '4', '1'), ('884', '815', '815', '1', '正则化参数', 'reg_param', 'floatInput', '0.0', '正则化参数', '正则化参数', '6', '{\"type\":\"float\",\"min\":0}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '1', '1', '1'), ('885', '815', '815', '1', '弹性参数', 'regparam_level', 'input', '0', '正则项弹性参数，取值范围[0,1]，取值0时，仅使用L2正则化，取值1时，仅使用L1正则化', '正则项弹性参数，取值范围[0,1]，取值0时，仅使用L2正则化，取值1时，仅使用L1正则化', '7', '{\"type\":\"float\",\"min\":0,\"max\":1}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '0', '1', '1'), ('886', '815', '815', '1', '模型路径', 'model_name', 'output', '', '模型训练完成后在HDFS上的存储路径', '模型训练完成后在HDFS上的存储路径', '5', '{}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '2', '1', '1', '1'), ('887', '815', '815', '1', '算法类别	', 'family', 'select', 'auto', '分类类别，包括二分类、多分类、自动推导', '分类类别，包括二分类、多分类、自动推导', '8', '{\"options\":[{\"key\":\"auto\",\"value\":\"auto\"},{\"key\":\"binomial\",\"value\":\"binomial\"},{\"key\":\"multinomial\",\"value\":\"multinomial\"}]}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '0', '1', '1'), ('888', '815', '815', '1', '权重列', 'weight_col', 'selectField', '', '训练表中的权重列', '训练表中的权重列', '4', '{\"limitSize\":1}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '0', '4', '1'), ('889', '815', '815', '1', '收敛精度', 'epsilon', 'floatInput', '0.000001', '算法最终的收敛精度。若两次迭代之间的误差小于该值，则停止迭代', '算法最终的收敛精度。若两次迭代之间的误差小于该值，则停止迭代', '9', '{\"type\":\"float\",\"min\":0,\"max\":1}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '0', '1', '1'), ('890', '815', '815', '1', '是否拟合截距', 'fit_intercept', 'boolean', 'True', '算法是否需要拟合线性模型中的截距项', '算法是否需要拟合线性模型中的截距项', '13', '{}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '0', '1', '1'), ('891', '815', '815', '1', '二分类阀值', 'threshold', 'input', '0.5', '大于或小于该值时分别为一类，仅当算法类别为二分类时有效', '大于或小于该值时分别为一类，仅当算法类别为二分类时有效', '10', '{}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '0', '1', '1'), ('892', '815', '815', '1', '聚合深度', 'aggregation_depth', 'intInput', '2', '数据聚合操作时，可以分多次聚合，避免因一次聚合将同一组数据加载到一个节点', '数据聚合操作时，可以分多次聚合，避免因一次聚合将同一组数据加载到一个节点', '11', '{\"type\":\"int\",\"min\":2}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '0', '1', '1'), ('893', '815', '815', '1', '迭代次数', 'max_iter', 'intInput', '10', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '训练模型时的最大迭代次数，若迭代超过该次数，则停止迭代', '12', '{\"type\":\"int\",\"min\":0}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '0', '1', '1'), ('894', '103', '103', '1', '文件路径', 'file_path', 'filePath', '', '文件路径，可以选择目录或文件', '文件路径，可以选择目录或文件', '1', '{}', '2018-11-17 17:02:33', '2018-11-17 17:02:33', '0', '1', '1', '1', '1'), ('896', '599', '599', '1', '保留列', 'remain_cols', 'selectField', '', '预测的结果表中所需保留待预测数据表中的列名', '', '3', '{}', '2018-11-19 13:36:48', '2018-11-19 13:36:48', '0', '3', '0', '4', '1'), ('899', '804', '804', '1', '分箱数', 'max_bins', 'input', '32', '离散连续特征时的最大分箱数，需要大于等于2并且大于等于离散特征的分类数', '离散连续特征时的最大分箱数，需要大于等于2并且大于等于离散特征的分类数', '13', '{\"type\":\"int\",\"min\":1}', '2018-11-19 15:10:18', '2018-11-19 15:10:18', '0', '3', '0', '1', '1'), ('900', '804', '804', '1', '子节点最小个数', 'min_instances_per_node', 'input', '1', '节点分割后子节点的最小个数，若分割后小于该数量，则取消分割', '节点分割后子节点的最小个数，若分割后小于该数量，则取消分割', '10', '{\"type\":\"int\",\"min\":1}', '2018-11-19 15:10:18', '2018-11-19 15:10:18', '0', '3', '1', '1', '1'), ('901', '804', '804', '1', '分割最小信息增益', 'min_info_gain', 'input', '0.0', '树节点分割时的最小信息增益', '树节点分割时的最小信息增益', '7', '{\"type\":\"float\",\"min\":0}', '2018-11-19 15:10:18', '2018-11-19 15:10:18', '0', '3', '0', '1', '1'), ('902', '804', '804', '1', '聚合时的最大内存', 'max_memory_in_mb', 'input', '256', '可选', '数据聚合时所分配的最大内存，单位为MB，如果值太小，则聚合时可能出错', '8', '{\"type\":\"int\",\"min\":1}', '2018-11-19 15:10:18', '2018-11-19 15:10:18', '0', '3', '0', '1', '1'), ('903', '804', '804', '1', '是否缓存实例ID', 'cache_node_ids', 'boolean', 'False', '数据聚合时所分配的最大内存，单位为MB，如果值太小，则聚合时可能出错', '如果是，则对每一个实例都缓存node ID, 加速训练。\r\n\r\n可以通过checkpoint_interval配置多久缓存一次，或者禁用', '9', '{}', '2018-11-19 15:10:18', '2018-11-19 15:10:18', '0', '3', '0', '1', '1'), ('904', '804', '804', '1', '检查点间隔', 'checkpoint_interval', 'input', '10', '设置检查间隔(>=1)或禁用(-1)；例如，设置为10，则每10次迭代会缓存一次配置', '设置检查间隔(>=1)或禁用(-1)；例如，设置为10，则每10次迭代会缓存一次配置', '11', '{\"type\":\"int\",\"min\":-1}', '2018-11-19 15:10:18', '2018-11-19 15:10:18', '0', '3', '0', '1', '1'), ('906', '804', '804', '1', '数据采样比例', 'subsampling_rate', 'select', '1.0', '训练每个决策树的数据占总训练数据的比例', '训练每个决策树的数据占总训练数据的比例', '15', '{\"type\":\"float\",\"max\":1}', '2018-11-19 15:10:18', '2018-11-19 15:10:18', '0', '3', '0', '1', '1'), ('907', '804', '804', '1', '特征采样策略', 'feature_subset_strategy', 'select', 'auto', '分割节点时，考虑特征的数量。', '分割节点时，考虑特征的数量。设为auto时，若num_trees==1，则设为all,若num_trees>1,则设为sqrt；\r\n\r\nall：使用所有特征；onethird：使用1/3特征；sqrt:使用sqrt(#features)；log2：使用log2(#features)', '14', '{\"options\":[{\"key\":\"auto\",\"value\":\"auto\"},{\"key\":\"all\",\"value\":\"all\"},{\"key\":\"onethird\",\"value\":\"onethird\"},{\"key\":\"sqrt\",\"value\":\"sqrt\"},{\"key\":\"log2\",\"value\":\"log2\"}]}', '2018-11-19 15:10:18', '2018-11-19 15:10:18', '0', '3', '0', '1', '1'), ('909', '805', '805', '1', '分箱数', 'max_bins', 'input', '32', '离散连续特征时的最大分箱数，需要大于等于2并且大于等于离散特征的分类数', '离散连续特征时的最大分箱数，需要大于等于2并且大于等于离散特征的分类数', '15', '{\"type\":\"int\",\"min\":1}', '2018-11-19 15:58:54', '2018-11-19 15:58:54', '0', '3', '0', '1', '1'), ('910', '805', '805', '1', '分割最小信息增益', 'min_info_gain', 'input', '0', '树节点分割时的最小信息增益', '树节点分割时的最小信息增益', '9', '{\"type\":\"float\",\"min\":0}', '2018-11-19 15:58:54', '2018-11-19 15:58:54', '0', '3', '0', '1', '1'), ('911', '805', '805', '1', '聚合时的最大内存', 'max_memory_in_mb', 'input', '256', '数据聚合时所分配的最大内存，单位为MB，如果值太小，则聚合时可能出错', '数据聚合时所分配的最大内存，单位为MB，如果值太小，则聚合时可能出错', '10', '{}', '2018-11-19 15:58:54', '2018-11-19 15:58:54', '0', '3', '0', '1', '1'), ('912', '805', '805', '1', '是否缓存实例ID', 'cache_node_ids', 'boolean', 'False', '如果是，则对每一个实例都缓存node ID, 加速训练。\r\n\r\n可以通过checkpoint_interval配置多久缓存一次，或者禁用', '如果是，则对每一个实例都缓存node ID, 加速训练。\r\n\r\n可以通过checkpoint_interval配置多久缓存一次，或者禁用', '11', '{}', '2018-11-19 15:58:54', '2018-11-19 15:58:54', '0', '3', '0', '1', '1'), ('913', '805', '805', '1', '检查点间隔', 'checkpoint_interval', 'input', '10', '设置检查间隔(>=1)或禁用(-1)；例如，设置为10，则每10次迭代会缓存一次配置', '设置检查间隔(>=1)或禁用(-1)；例如，设置为10，则每10次迭代会缓存一次配置', '13', '{\"type\":\"int\",\"min\":-1}', '2018-11-19 15:58:54', '2018-11-19 15:58:54', '0', '3', '0', '1', '1'), ('914', '805', '805', '1', '学习步长', 'step_size', 'input', '0.1', '学习步长', '学习步长', '8', '{\"type\":\"float\",\"min\":0,\"max\":1}', '2018-11-19 15:58:54', '2018-11-19 15:58:54', '0', '3', '0', '1', '1'), ('917', '506', '506', '1', '弹性参数', 'elastic_net_param', 'input', '0.0', '正则项弹性参数，取值范围[0,1]，取值0时，仅使用L2正则化，取值1时，仅使用L1正则化\r\n\r\n正则项弹性参数，取值范围[0,1]，取值0时，仅使用L2正则化，取值1时，仅使用L1正则化\r\n\r\n', '正则项弹性参数，取值范围[0,1]，取值0时，仅使用L2正则化，取值1时，仅使用L1正则化\r\n\r\n正则项弹性参数，取值范围[0,1]，取值0时，仅使用L2正则化，取值1时，仅使用L1正则化\r\n\r\n正则项弹性参数，取值范围[0,1]，取值0时，仅使用L2正则化，取值1时，仅使用L1正则化\r\n\r\n正则项弹性参数，取值范围[0,1]，取值0时，仅使用L2正则化，取值1时，仅使用L1正则化\r\n\r\n正则项弹性参数，取值范围[0,1]，取值0时，仅使用L2正则化，取值1时，仅使用L1正则化', '9', '{\"type\":\"float\",\"min\":0,\"max\":1}', '2018-11-19 16:50:46', '2018-11-19 16:50:46', '0', '3', '0', '1', '1'), ('918', '506', '506', '1', '收敛精度', 'tol', 'input', '0.000001', '算法最终的收敛精度。若两次迭代之间的误差小于该值，则停止迭代', '算法最终的收敛精度。若两次迭代之间的误差小于该值，则停止迭代', '10', '{\"type\":\"float\",\"min\":0}', '2018-11-19 16:50:46', '2018-11-19 16:50:46', '0', '3', '0', '1', '1'), ('919', '506', '506', '1', '是否拟合截距', 'fit_intercept', 'boolean', 'True', '算法是否需要拟合线性模型中的截距项', '算法是否需要拟合线性模型中的截距项', '14', '{}', '2018-11-19 16:50:46', '2018-11-19 16:50:46', '0', '3', '0', '1', '1'), ('920', '506', '506', '1', '是否标准化', 'standardization', 'boolean', 'True', '在训练模型前是否对训练特征进行标准化', '在训练模型前是否对训练特征进行标准化', '15', '{}', '2018-11-19 16:50:46', '2018-11-19 16:50:46', '0', '3', '0', '1', '1'), ('921', '506', '506', '1', '权重列', 'weight_col', 'selectField', '', '样本对应的权重。若不设置，则认为所有样本权重相同', '样本对应的权重。若不设置，则认为所有样本权重相同', '4', '{}', '2018-11-19 16:50:46', '2018-11-19 16:50:46', '0', '3', '0', '4', '1'), ('922', '506', '506', '1', '聚合深度', 'aggregation_depth', 'input', '2', '数据聚合操作时，可以分多次聚合，避免因一次聚合将同一组数据加载到一个节点', '数据聚合操作时，可以分多次聚合，避免因一次聚合将同一组数据加载到一个节点', '12', '{\"type\":\"float\",\"min\":2}', '2018-11-19 16:50:46', '2018-11-19 16:50:46', '0', '3', '0', '1', '1'), ('923', '506', '506', '1', '损失函数类型', 'loss', 'select', 'squaredError', '损失函数类型，squaredError: squared loss, huber: huber loss', '损失函数类型，squaredError: squared loss, huber: huber loss', '6', '{\"options\":[{\"key\":\"squaredError\",\"value\":\"squaredError\"},{\"key\":\"huber\",\"value\":\"huber\"}]}', '2018-11-19 16:50:46', '2018-11-19 16:50:46', '0', '3', '0', '1', '1'), ('926', '103', '103', '1', '文件类型', 'file_type', 'select', 'text', '文件类型', '', '2', '{\"options\":[{\"value\":\"text\",\"key\":\"text\"}, {\"value\":\"csv\",\"key\":\"csv\"}, {\"value\":\"orc\",\"key\":\"orc\"}, {\"value\":\"parquet\",\"key\":\"parquet\"}, {\"value\":\"other\",\"key\":\"其他\"}]}', '2018-11-19 19:01:17', '2018-11-19 19:01:17', '0', '1', '1', '1', '1'), ('927', '605', '90001', '1', 'Python代码文件', '__codeFiles', 'codeUploader', '', '支持.py、.pyc文件和.tgz、.tar、.zip、.tar.gz后缀的压缩文件', '', '1', '', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '3', '1', '1', '1'), ('928', '605', '90001', '1', 'Python主文件', '__launchFile', 'input', '', '如果上传压缩包填写相对压缩包内部目录结构的相对路径', '', '1', '', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '3', '1', '1', '1'), ('929', '605', '90001', '1', '超参配置', '__launchArgs', 'keyValue', '', '超参配置将在运行时通过命令行参数传入启动脚本', '', '1', '', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '3', '0', '1', '1'), ('930', '605', '90001', '1', '样本数据', 'input_path', 'input', '', '样本数据，端点不再页面展示', '', '1', '', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '2', '1', '1', '1'), ('931', '605', '90001', '1', '深度学习模型', 'model_path', 'input', '', '模型路径，端点不再页面展示', '', '1', '', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '2', '1', '1', '1'), ('932', '605', '90001', '1', '输出路径', 'output_path', 'input', '', '输出路径，端点不再页面展示', '', '1', '', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '2', '1', '1', '1'), ('933', '605', '90000', '2', 'Worker个数', 'worker_num', 'intInput', '1', 'worker服务器个数', '', '1', '{}', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '3', '0', '1', '1'), ('934', '605', '90000', '2', 'Worker CPU核数', 'worker_cores', 'intInput', '1', '每个worker服务器cpu核数', '', '1', '{}', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '3', '0', '1', '1'), ('935', '605', '90000', '2', 'Worker GPU核数', 'worker_gcores', 'intInput', '0', '每个worker服务器gpu核数', '', '1', '{}', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '3', '0', '1', '1'), ('936', '605', '90000', '2', 'Worker内存', 'worker_memory', 'input', '2G', '每个worker服务器内存大小，支持单位(G、M、K)', '', '1', '{}', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0', '3', '0', '1', '1'), ('937', '815', '815', '1', '是否标准化', 'standardization', 'boolean', 'True', '在训练模型前是否对训练特征进行标准化', '在训练模型前是否对训练特征进行标准化', '14', '{}', '2018-11-16 21:23:11', '2018-11-16 21:23:11', '0', '3', '0', '1', '1'), ('938', '511', '511', '1', '聚类中心表', 'center_table', 'input', null, '聚类中心表', null, '1', '{}', '2018-11-22 11:27:46', '2018-11-22 11:27:46', '0', '2', '0', '1', '1'), ('939', '209', '209', '1', '待编码数据表', 'input_table', 'input', '', '待编码数据表', '', '1', '{}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '2', '1', '1', '1'), ('940', '209', '209', '1', '输入映射表', 'input_model_table', 'input', '', '如果未设置该表，则直接用输入数据建立编码规则，若设置了该表，则以该表为映射表，直接生成编码', '', '1', '{}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '2', '0', '1', '1'), ('941', '209', '209', '1', '编码结果表', 'output_table', 'input', '', '编码后的结果表', '', '1', '{}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '2', '1', '1', '1'), ('942', '209', '209', '1', '输出映射表', 'output_mapping_table', 'input', '', '编码与原始值的映射表', '', '1', '{}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '2', '1', '1', '1'), ('943', '209', '209', '1', '待编码列', 'selected_cols', 'selectField', '', '需要进行编码的列', '', '1', '{}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '3', '1', '4', '1'), ('944', '209', '209', '1', '额外编码列', 'reserve_cols', 'selectField', '', '需要作为特征（不做one-hot编码）输出到kv字段中，保留的特征会从0开始编码。必须是long、int或double类型', '', '1', '{}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '3', '0', '4', '1'), ('945', '209', '209', '1', '保留列', 'append_cols', 'selectField', '', '需要保持不变输出到结果表中的列', '', '1', '{}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '3', '0', '4', '1'), ('946', '209', '209', '1', '输出表类型', 'output_table_type', 'select', 'kv', '稀疏(kv)或稠密表(table)，当离散特征较多时，建议输出kv格式，table 仅支持512列，超出将报错', '', '1', '{\"options\":[{\"key\":\"kv\",\"value\":\"kv\"},{\"key\":\"table\",\"value\":\"table\"}]}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '3', '0', '1', '1'), ('947', '209', '209', '1', '是否删除最后一个编码', 'drop_last', 'boolean', 'false', '是否删除最后一个编码', '', '1', '{}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '3', '0', '1', '1'), ('948', '209', '209', '1', '项分隔符', 'item_delimiter', 'input', ' ', '键值对与键值对之间的分隔符。若特征为稀疏存储，才可以对其进行设置', '', '1', '{}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '3', '0', '1', '1'), ('949', '209', '209', '1', '键值对分隔符', 'kv_delimiter', 'input', ':', '键与值之间的分隔符。若特征为稀疏存储，才可以对其进行设置', '', '1', '{}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '3', '0', '1', '1'), ('950', '81', '90001', '1', '数据表', 'table', 'input', null, '数据表', '保存的数据表', '1', '{}', '2018-11-22 22:19:09', '2018-11-22 22:19:09', '0', '3', '1', '1', '1'), ('951', '81', '90001', '1', '表存储类型', 'table_format', 'select', 'parquet', '数据表的存储格式', '数据表的存储格式', '2', '{\"options\":[{\"value\":\"text\",\"key\":\"text\"}, {\"value\":\"csv\",\"key\":\"csv\"}, {\"value\":\"orc\",\"key\":\"orc\"}, {\"value\":\"parquet\",\"key\":\"parquet\"}, {\"value\":\"other\",\"key\":\"其他\"}]}', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0', '3', '0', '1', '1'), ('952', '81', '90001', '1', '文件类型', 'file_type', 'select', 'text', '数据表的存储格式', '数据表的存储格式', '3', '{\"options\":[{\"value\":\"text\",\"key\":\"text\"}, {\"value\":\"csv\",\"key\":\"csv\"}, {\"value\":\"orc\",\"key\":\"orc\"}, {\"value\":\"parquet\",\"key\":\"parquet\"}, {\"value\":\"other\",\"key\":\"其他\"}]}', '2018-11-22 22:23:07', '2018-11-22 22:23:07', '0', '3', '0', '1', '1'), ('953', '81', '90001', '1', '文件路径', 'file_path', 'input', null, '文件路径', '文件路径', '4', null, '2018-11-22 22:24:45', '2018-11-22 22:24:45', '0', '2', '1', '1', '1'), ('954', '81', '90001', '1', '字段定义', 'field_def', 'input', null, '自定义输出表的字段', '自定义输出表的字段', '6', null, '2018-11-22 22:29:20', '2018-11-22 22:29:20', '0', '2', '0', '1', '1'), ('955', '81', '90001', '1', '忽略表头', 'header', 'boolean', null, '读取文件时表头不作为列名', '读取文件时表头不作为列名', '7', null, '2018-11-22 22:30:18', '2018-11-22 22:30:18', '0', '3', '0', '1', '1'), ('956', '81', '90001', '1', '分隔符', 'field_delim', 'input', '\\t', 'text文件格式时表示字段分隔符', 'text文件格式时表示字段分隔符', '5', null, '2018-11-22 22:39:34', '2018-11-22 22:39:34', '0', '3', '0', '1', '1'), ('958', '601', '90000', '2', 'PS个数', 'ps_num', 'intInput', '0', '参数服务器个数，ps个数大于表示分布式模式', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('959', '601', '90000', '2', 'PS CPU核数', 'ps_cores', 'intInput', '0', '每个参数服务器cpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('960', '601', '90000', '2', 'PS内存', 'ps_memory', 'input', '0', '每个参数服务器内存大小，支持单位(G、M、K)', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('961', '601', '90000', '2', 'Worker个数', 'worker_num', 'intInput', '1', 'worker服务器个数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('962', '601', '90000', '2', 'Worker CPU核数', 'worker_cores', 'intInput', '1', '每个worker服务器cpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('963', '601', '90000', '2', 'Worker GPU核数', 'worker_gcores', 'intInput', '0', '每个worker服务器gpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('964', '601', '90000', '2', 'Worker内存', 'worker_memory', 'input', '2G', '每个worker服务器内存大小，支持单位(G、M、K)', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('965', '602', '90000', '2', 'PS个数', 'ps_num', 'intInput', '0', '参数服务器个数，ps个数大于表示分布式模式', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('966', '602', '90000', '2', 'PS CPU核数', 'ps_cores', 'intInput', '0', '每个参数服务器cpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('967', '602', '90000', '2', 'PS内存', 'ps_memory', 'input', '0', '每个参数服务器内存大小，支持单位(G、M、K)', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('968', '602', '90000', '2', 'Worker个数', 'worker_num', 'intInput', '1', 'worker服务器个数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('969', '602', '90000', '2', 'Worker CPU核数', 'worker_cores', 'intInput', '1', '每个worker服务器cpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('970', '602', '90000', '2', 'Worker GPU核数', 'worker_gcores', 'intInput', '0', '每个worker服务器gpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('971', '602', '90000', '2', 'Worker内存', 'worker_memory', 'input', '2G', '每个worker服务器内存大小，支持单位(G、M、K)', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('972', '603', '90000', '2', 'PS个数', 'ps_num', 'intInput', '0', '参数服务器个数，ps个数大于表示分布式模式', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('973', '603', '90000', '2', 'PS CPU核数', 'ps_cores', 'intInput', '0', '每个参数服务器cpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('974', '603', '90000', '2', 'PS内存', 'ps_memory', 'input', '0', '每个参数服务器内存大小，支持单位(G、M、K)', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('975', '603', '90000', '2', 'Worker个数', 'worker_num', 'intInput', '1', 'worker服务器个数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('976', '603', '90000', '2', 'Worker CPU核数', 'worker_cores', 'intInput', '1', '每个worker服务器cpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('977', '603', '90000', '2', 'Worker GPU核数', 'worker_gcores', 'intInput', '0', '每个worker服务器gpu核数', '', '1', '{}', '2018-11-13 14:27:24', '2018-11-13 14:27:24', '0', '3', '0', '1', '1'), ('978', '603', '90000', '2', 'Worker内存', 'worker_memory', 'input', '2G', '每个worker服务器内存大小，支持单位(G、M、K)', 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'2018-11-05 21:12:25', '2018-11-05 21:12:25', '0'), ('94', '804', '输入训练表', '1', '输入表', 'input', 'input_table', '1', '2018-11-05 21:12:25', '2018-11-05 21:12:25', '0'), ('98', '205', '数据表', '1', '用于评估特征重要性的数据表，需要包含模型训练时所需要的特征及标签', 'input', 'input_table', '1', '2018-11-06 10:56:02', '2018-11-06 10:56:02', '0'), ('99', '205', '输入模型路径', '1', '使用随机森林分类或随机森林回归组件生成的模型路径', 'input', 'model_name', '4', '2018-11-06 10:56:02', '2018-11-06 10:56:02', '0'), ('100', '205', '特征重要性结果表', '1', '各个特征的重要性结果', 'output', 'output_table', '1', '2018-11-06 10:56:02', '2018-11-06 10:56:02', '0'), ('101', '703', 'P(z/d)输出表', '1', 'P(z/d)输出表', 'output', 'pzd_table', '1', '2018-11-06 17:33:00', '2018-11-06 17:33:00', '0'), ('102', '703', 'P(w/z)输出表', '2', 'P(w/z)输出表', 'output', 'pwz_table', '1', '2018-11-06 17:33:00', '2018-11-06 17:33:00', '0'), ('103', '703', '输入表', '1', '输入表', 'input', 'input_table', '1', '2018-11-06 17:33:00', '2018-11-06 17:33:00', '0'), ('104', '207', '数据表', '1', '需要进行奇异值分解的数据表', 'input', 'input_table', '1', '2018-11-06 18:04:42', '2018-11-06 18:04:42', '0'), ('105', '207', '左特征向量表', '1', '左特征向量表', 'output', 'output_u_table', '1', '2018-11-06 18:04:42', '2018-11-06 18:04:42', '0'), ('106', '207', '奇异值表', '2', '奇异值表', 'output', 'output_s_table', '1', '2018-11-06 18:04:42', '2018-11-06 18:04:42', '0'), ('107', '207', '右特征向量表', '3', '右特征向量表', 'output', 'output_v_table', '1', '2018-11-06 18:04:42', '2018-11-06 18:04:42', '0'), ('108', '208', '数据表', '1', '用于评估特征重要性的数据表，需要包含模型训练时所需要的特征及标签	', 'input', 'input_table', '1', '2018-11-07 13:22:35', '2018-11-07 13:22:35', '0'), ('109', '208', '输入模型路径', '1', '使用GBDT回归和GBDT二分类生成的模型路径', 'input', 'model_name', '4', '2018-11-07 13:22:35', '2018-11-07 13:22:35', '0'), ('110', '208', '特征重要性结果表	', '1', '各个特征的重要性结果', 'output', 'output_table', '1', '2018-11-07 13:22:35', '2018-11-07 13:22:35', '0'), ('114', '805', '输出模型', '1', '输出模型', 'output', 'model_name', '4', '2018-11-08 15:44:36', '2018-11-08 15:44:36', '0'), ('115', '805', '输出表', '2', '表输重要性', 'output', 'output_importance_table', '1', '2018-11-08 15:44:36', '2018-11-08 15:44:36', '1'), ('116', '805', '输入表', '1', '输入数据', 'input', 'input_table', '1', '2018-11-08 15:44:36', '2018-11-08 15:44:36', '0'), ('123', '502', '输入训练表', '1', '输入表', 'input', 'input_table', '1', '2018-11-08 20:17:46', '2018-11-08 20:17:46', '0'), ('125', '508', '输入评估数据表', '1', '输入表', 'input', 'input_table', '1', '2018-11-08 20:24:46', '2018-11-08 20:24:46', '0'), ('126', '508', '输入聚类模型', '2', '输入模型', 'input', 'model_name', '4', '2018-11-08 20:24:46', '2018-11-08 20:24:46', '0'), ('127', '508', '输出评估结果表', '1', '输出表', 'output', 'output_table', '1', '2018-11-08 20:24:46', '2018-11-08 20:24:46', '0'), ('128', '711', '输入表', '1', '输入表名', 'input', 'input_table', '1', '2018-11-08 20:44:03', '2018-11-08 20:44:03', '0'), ('129', '711', '输出表', '1', '输出表名', 'output', 'output_table', '1', '2018-11-08 20:44:03', '2018-11-08 20:44:03', '0'), ('137', '803', '输出模型', '2', '输出模型', 'output', 'model_name', '4', '2018-11-08 21:57:20', '2018-11-08 21:57:20', '0'), ('138', '803', '输入训练表', '1', '输入表名', 'input', 'input_table', '1', '2018-11-08 21:57:20', '2018-11-08 21:57:20', '0'), ('139', '102', '输入表', '1', '输入表', 'input', 'input_table', '1', '2018-11-09 11:31:52', '2018-11-09 11:31:52', '1'), ('140', '102', '输出表', '1', '表输出', 'output', 'output_table', '1', '2018-11-09 11:31:52', '2018-11-09 11:31:52', '0'), ('141', '101', '输入表', '1', '输入表', 'input', 'input_table', '1', '2018-11-09 11:33:15', '2018-11-09 11:33:15', '0'), ('142', '101', '输出表', '1', '表输出', 'output', 'output_table', '1', '2018-11-09 11:33:15', '2018-11-09 11:33:15', '0'), ('143', '600', '样本数据', '1', '样本数据', 'input', 'input_path', '2', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0'), ('145', '600', '输出路径', '1', '输出路径', 'output', 'output_path', '4', '2018-11-09 11:35:45', '2018-11-09 11:35:45', '0'), ('146', '601', '样本数据', '1', '样本数据', 'input', 'input_path', '2', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0'), ('148', '601', '输出路径', '1', '输出路径', 'output', 'output_path', '4', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0'), ('149', '602', '样本数据', '1', '样本数据', 'input', 'input_path', '2', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0'), ('151', '602', '输出路径', '1', '输出路径', 'output', 'output_path', '4', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0'), ('152', '603', '样本数据', '1', '样本数据', 'input', 'input_path', '2', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0'), ('154', '603', '输出路径', '1', '输出路径', 'output', 'output_path', '4', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0'), ('155', '604', '样本数据', '1', '样本数据', 'input', 'input_path', '2', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0'), ('157', '604', '输出路径', '1', '输出路径', 'output', 'output_path', '4', '2018-11-09 21:02:33', '2018-11-09 21:02:33', '0'), ('158', '599', '输出预测结果表', '1', '输出预测结果表', 'output', 'output_table', '1', '2018-11-10 14:52:31', '2018-11-10 14:52:31', '0'), ('159', '599', '待预测数据表', '1', '输入待预测数据表', 'input', 'input_table', '1', '2018-11-10 14:52:31', '2018-11-10 14:52:31', '0'), ('160', '599', '输入机器学习模型', '2', '输入机器学习模型', 'input', 'model_name', '4', '2018-11-10 14:52:31', '2018-11-10 14:52:31', '0'), ('161', '515', '输出详细表', '1', '输出的指标表', 'output', 'output_metric_table', '1', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0'), ('162', '515', '输出指标表', '2', '输出用于画图的详细数据表', 'output', 'output_detail_table', '1', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0'), ('163', '515', '预测结果表', '1', '输入表', 'input', 'input_table', '1', '2018-11-12 15:50:34', '2018-11-12 15:50:34', '0'), ('164', '713', '输出表', '1', '表输出', 'output', 'output_table', '1', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0'), ('165', '713', '输入表', '1', '输入表', 'input', 'input_table', '1', '2018-11-12 17:03:13', '2018-11-12 17:03:13', '0'), ('168', '714', '输入表', '1', '输入表', 'input', 'input_table_name', '1', '2018-11-12 17:15:01', '2018-11-12 17:15:01', '0'), ('169', '714', '输出表', '1', '输出表', 'output', 'output_table_name', '1', '2018-11-12 17:15:01', '2018-11-12 17:15:01', '0'), ('170', '502', '输出模型', '1', '输出模型文件路径', 'output', 'model_name', '4', '2018-11-12 17:16:31', '2018-11-12 17:16:31', '0'), ('171', '80', '输出表', '1', '输出表名', 'output', 'output_table', '4', '2018-11-12 17:58:23', '2018-11-12 17:58:23', '0'), ('172', '707', '输出表', '1', '输出表', 'output', 'output_table', '1', '2018-11-12 19:12:04', '2018-11-12 19:12:04', '0'), ('173', '707', '输入表', '1', '输入表', 'input', 'input_table', '1', '2018-11-12 19:12:04', '2018-11-12 19:12:04', '0'), ('174', '511', '输出模型', '1', '输出模型', 'output', 'model_name', '4', '2018-11-13 17:12:48', '2018-11-13 17:12:48', '0'), ('175', '511', '输出聚类结果表', '2', '输出聚类结果表', 'output', 'idx_table', '1', '2018-11-13 17:12:48', '2018-11-13 17:12:48', '0'), ('176', '511', '输入表', '1', '输入表', 'input', 'input_table', '1', '2018-11-13 17:12:48', '2018-11-13 17:12:48', '0'), ('177', '506', '输入训练表', '1', '输入表', 'input', 'input_table', '1', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0'), ('178', '506', '输出模型', '1', '输出模型', 'output', 'model_name', '4', '2018-11-13 21:10:25', '2018-11-13 21:10:25', '0'), ('179', '715', '输入表', '1', '输入表', 'input', 'input_table', '1', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0'), ('180', '715', '输入已有的索引表', '2', '输入已有的索引表', 'input', 'index_input_table', '1', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0'), ('181', '715', '输出kv表名', '1', '输出kv表名', 'output', 'output_table', '1', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0'), ('182', '715', '输出key的索引表', '2', '输出key的索引表', 'output', 'index_output_table', '1', '2018-11-13 23:26:28', '2018-11-13 23:26:28', '0'), ('183', '808', '输出模型', '1', '输出模型', 'output', 'model_name', '4', '2018-11-16 19:40:08', '2018-11-16 19:40:08', '0'), ('184', '808', '输入表', '1', '输入表', 'input', 'input_table', '1', '2018-11-16 19:40:08', '2018-11-16 19:40:08', '0'), ('185', '809', '输出模型', '1', '输出模型', 'output', 'model_name', '4', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0'), ('186', '809', '输入训练表', '1', '输入表', 'input', 'input_table', '1', '2018-11-16 20:04:32', '2018-11-16 20:04:32', '0'), ('187', '810', '输出模型', '4', '输出模型', 'output', 'model_name', '4', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0'), ('188', '810', '输出聚类结果表', '1', '输出表名', 'output', 'idx_table', '1', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0'), ('189', '810', '输出聚类统计表', '2', '输出表名', 'output', 'cluster_count_table', '1', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0'), ('190', '810', '高斯分布表', '3', '输出表名', 'output', 'distribut_table', '1', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0'), ('191', '810', '输入表', '1', '输入表', 'input', 'input_table', '1', '2018-11-16 20:23:22', '2018-11-16 20:23:22', '0'), ('194', '815', '输入训练表', '1', '输入表名', 'input', 'input_table', '1', '2018-11-16 21:00:36', '2018-11-16 21:00:36', '0'), ('195', '815', '输出模型', '1', '输出模型路径', 'output', 'model_name', '4', '2018-11-16 21:00:36', '2018-11-16 21:00:36', '0'), ('196', '816', '输入训练表', '1', '输入表名', 'input', 'input_table', '1', '2018-11-16 21:04:44', '2018-11-16 21:04:44', '0'), ('197', '816', '输出模型', '1', '输出模型路径', 'output', 'model_name', '4', '2018-11-16 21:04:44', '2018-11-16 21:04:44', '0'), ('198', '103', '文件路径', '1', '文件路径', 'output', 'file_path', '2', '2018-11-17 17:02:33', '2018-11-17 17:02:33', '0'), ('200', '511', '输出聚类统计表', '4', '输出聚类统计表', 'output', 'cluster_count_table', '1', '2018-11-19 20:29:08', '2018-11-19 20:29:08', '0'), ('201', '511', '输出聚类中心表', '3', '输出聚类中心表', 'output', 'center_table', '1', '2018-11-19 20:29:08', '2018-11-19 20:29:08', '0'), ('202', '605', '样本数据', '1', '样本数据', 'input', 'input_path', '2', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0'), ('203', '605', '深度学习模型', '1', '深度学习模型', 'input', 'model_path', '4', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0'), ('204', '605', '输出路径', '1', '输出路径', 'output', 'output_path', '2', '2018-11-20 16:52:07', '2018-11-20 16:52:07', '0'), ('205', '209', '待编码数据表', '1', '待编码数据表', 'input', 'input_table', '1', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0'), ('206', '209', '输入映射表', '2', '如果未设置该表，则直接用输入数据建立编码规则，若设置了该表，则以该表为映射表，直接生成编码', 'input', 'input_model_table', '1', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0'), ('207', '209', '编码结果表', '1', '编码后的结果表', 'output', 'output_table', '1', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0'), ('208', '209', '输出映射表', '2', '编码与原始值的映射表', 'output', 'output_mapping_table', '1', '2018-11-22 15:38:46', '2018-11-22 15:38:46', '0'), ('209', '81', '输入文件', '1', '输入的文件', 'input', 'file_path', '2', '2018-11-22 22:31:36', '2018-11-22 22:31:36', '0'), ('210', '81', '数据表', '2', '数据表', 'output', 'table', '1', '2018-11-22 22:32:06', '2018-11-22 22:32:06', '0'), ('211', '711', '停用词表', '2', '停用词表', 'input', 'stopwords_table', '1', '2018-11-24 16:01:27', '2018-11-24 16:01:27', '1');
COMMIT;


UPDATE `bas_node_config` SET `value`=' ' WHERE `code`='item_delimiter' AND `node_id` = 715;

UPDATE bas_node set code ="sql" where node_id=3;

SET FOREIGN_KEY_CHECKS = 1;


#下线数据表组件
update bas_node set invalid=1 where node_id=1;

#读写数据表组件的名称里去掉组件两字
update bas_node set name="表转文件" where node_id=4;
update bas_node set name="读模型" where node_id=80;
update bas_node set name="文件转表" where node_id=81;
update bas_node set name="写数据表" where node_id=101;
update bas_node set name="读数据表" where node_id=102;
update bas_node set name="读文件" where node_id=103;
update bas_node set name="机器学习预测" where node_id=599;
update bas_node set name="深度学习预测" where node_id=605;


#组件添加配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('9','9','1','分隔符','separator','input','\t','分隔符','','1','{}','3','0',1);

INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('703','703','1','最大词项','max_terms_per_topic','intInput','10','描述每个topic的最大词项数量','','1','{}','3','0',1);

INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('709','709','1','句子分隔符','split_str','input','.!?。！？,，','句子分隔符','','1','{}','3','0',1);




update bas_node_config set param ='{"limitSize":1}' where  node_id =11 and code ='prob_col';
update rel_node_port  set name ='输出词频统计表' where node_id ='700' and code ='output_table_name_triple';
update rel_node_port  set name ='输出保序词语表' where node_id ='700' and code ='output_table_name_multi';
update rel_node_port  set name =descr where node_id ='715';
update rel_node_port  set name =descr where node_id ='703';


update bas_node set invalid='1' where code ='textcnn';
update bas_node_config set required='0' where node_id=14 and code in ('split_type','threshold_column','threshold_val','proportion');





update bas_node_config set name ='负采样个数' where node_id =706 and code='negative';
update bas_node_config set value ='3' where node_id =703 and code='topic_num';
update bas_node_config set value ='1.0',tip = 'P(z/d)的先验狄利克雷分布的参数，必须>1.0' , param ='{"type":"float"}'  where node_id =703 and code='alpha';
update bas_node_config set value ='1.0' ,tip='P(w/z)的先验狄利克雷分布的参数，必须>1.0'  , param ='{"type":"float"}'  where node_id =703 and code='beta';
update bas_node_config set tip="当前采用覆盖写入方式，请慎重选择已存在的表(或分区)" where node_id=101 and code="write_data_table";
update bas_node_config set `value`='' ,required='1' where node_id =703 and `code`='topic_num';
update bas_node_config set value="" where node_id=7 and code="index_table";
update bas_node_config set value="" where node_id=700 and code="output_table_name_multi";
update bas_node_config set value="" where node_id=700 and code="output_table_name_triple";
UPDATE `bas_node_config` SET `value`=' ' WHERE `code`='item_delimiter' AND `node_id` = 715;
UPDATE bas_node set code ="sql" where node_id=3;


DROP TABLE IF EXISTS `bas_node_tab_link`;
CREATE TABLE `bas_node_tab_link` (
                                   `id` bigint(19) NOT NULL AUTO_INCREMENT,
 `node_id` bigint(19) DEFAULT NULL COMMENT '组件id',
 `tab_id` bigint(19) DEFAULT NULL COMMENT '配置组id',
 `ctime` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
 `mtime` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '修改时间',
 `invalid` tinyint(2) NOT NULL DEFAULT '0' COMMENT '0:有效; 1:无效',
 PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=128 DEFAULT CHARSET=utf8 COMMENT='组件tab关联表';



# 表rel_node_config_link新增字段#(parent_code)
ALTER TABLE rel_node_config_link ADD required tinyint(2) DEFAULT 1;

-- 表bas_bare_model新增字段：engine_id
ALTER TABLE bas_bare_model ADD engine_id BIGINT(19);

# 表rel_instance_config_value新增字段#(parent_code)
ALTER TABLE rel_instance_config_value ADD parent_code VARCHAR(128) DEFAULT NULL;
# 表bas_instance_input_port新增字段#(config_id)
ALTER TABLE bas_instance_input_port ADD config_id BIGINT(19);
# 表bas_instance_output_port新增字段#(config_id)
ALTER TABLE bas_instance_output_port ADD config_id BIGINT(19);
# 表rel_instance_graph新增字段#(input_config_id,output_config_id)
ALTER TABLE rel_instance_graph ADD input_config_id BIGINT(19);
ALTER TABLE rel_instance_graph ADD output_config_id BIGINT(19);

# 写表组件写入分区非必填
update bas_node_config set required=0 where node_id=101 and code='output_partition';
update rel_instance_config_value set required=0 where node_id=101 and code='output_partition';


# 表bas_node_instance新增索引#(`node_id`)
alter table bas_node_instance add index index_flowid(`flow_id`);
alter table bas_node_instance add index index_nodeid(`node_id`);

# 表bas_node_config新增索引#(`node_id`)
alter table bas_node_config add index index_nodeid(`node_id`);
# 表bas_node_config新增索引#(`node_code`)
alter table bas_node_config add index index_code(`code`);


# 表rel_instance_config_value新增唯一索引#(`instance_id`, `code`)
alter table rel_instance_config_value add unique index_instance_code(`instance_id`, `code`, `invalid`);

# 表bas_instance_output_port新增索引#(`instance_id`)
alter table bas_instance_output_port add index index_instanceid(`instance_id`);
# 表bas_instance_output_port新增索引#(`instance_id`)
alter table bas_instance_output_port add index index_configid(`config_id`);
# 表bas_instance_output_port新增索引#(`flow_id`)
alter table bas_instance_output_port add index index_flowid(`flow_id`);


# 表bas_instance_input_port新增索引(`instance_id`)
alter table bas_instance_input_port add index index_instanceid(`instance_id`);
# 表bas_instance_output_port新增索引#(`instance_id`)
alter table bas_instance_input_port add index index_configid(`config_id`);
# 表bas_instance_output_port新增索引#(`flow_id`)
alter table bas_instance_input_port add index index_flowid(`flow_id`);


# 表rel_instance_graph新增索引(`source`)
alter table rel_instance_graph add index index_source(`source`);
# 表rel_instance_graph新增索引#(`target`)
alter table rel_instance_graph add index index_target(`target`);
# 表rel_instance_graph新增索引#(`flow_id`)
alter table rel_instance_graph add index index_flowid(`flow_id`);
# 表rel_instance_graph新增索引#(`input_config_id`)
alter table rel_instance_graph add index index_inputconfigid(`input_config_id`);
# 表rel_instance_graph新增索引#(`output_config_id`)
alter table rel_instance_graph add index index_outputconfigid(`output_config_id`);


# 表rel_instance_position新增索引#(`instance_id`)
alter table rel_instance_position add index index_instanceid(`instance_id`);


# 自定义PySpark组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`, `select_filed_type`, `predict_type`) values
(901, 8, "PySpark", "pyspark", "PySpark自定义代码", 8, 2, 8, 1, 1, 2);
# 组件参数
insert into `bas_node_config`

 ( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`, `parsing_rule`, `model_op_rule`)
 values
( '901', '90001', '1', 'Spark版本', '__engineVersion', 'select', '2.3', '支持多种不同版本的pyspark', '', '1', '{\"options\":[{\"value\":\"1.6\",\"key\":\"V1.6\"},{\"value\":\"2.3\",\"key\":\"V2.3\"}]}','3', '1', '1', '1'),
( '901', '90001', '1', 'Python环境', '__launchVenv', 'select', 'spark_default_py2', '支持选择含不同python库的python环境', '', '2', '{\"options\":[{\"value\":\"spark_default_py2\",\"key\":\"spark默认环境(python2.7)\"}]}','2', '1', '1', '1'),
( '901', '90001', '1', 'Python代码文件', '__codeFiles', 'codeUploader', '', '支持.py、.pyc文件和.zip后缀的压缩文件', '', '3', '{}',
'3', '1', '1', '1'),
( '901', '90001', '1', 'Python主文件', '__launchFile', 'input', '', '如果上传压缩包填写相对压缩包内部目录结构的相对路径', '', '4', '{}','3', '0',
 '1', '1'),
( '901', '90001', '1', '输入源', 'input_port_args', 'inputPortEdit', '', '输入源端口设置', '', '5',
'{\"options\":[{\"value\":\"1\",\"key\":\"数据表\"}]}',  '1', '0', '1', '1'),
( '901', '90001', '1', '输出源', 'output_port_args', 'outputPortEdit', '', '输出源端口设置', '', '6',
'{\"options\":[{\"value\":\"1\",\"key\":\"数据表\"}]}', '1', '0', '1', '1'),
( '901', '90001', '1', '超参配置', '__launchArgs', 'keyValue', '', '超参配置将在运行时通过命令行参数传入启动脚本', '', '7', '{}','3', '0', '1', '1'),
( '901', '90000', '2', 'Driver CPU核数', 'driver_cores', 'intInput', '1', 'driver所需的cpu核心个数', '', '1', '{}','3', '1', '1', '1'),
( '901', '90000', '2', 'Driver 内存', 'driver_memory', 'unitInput', '1G', 'driver所需的内存个数，支持单位(G、M、K)', '', '2', '{"options":[{"value":"G","key":"G"},{"value":"M","key":"M"},{"value":"K","key":"K"}]}','3',
'1', '1', '1'),
( '901', '90000', '2', 'AppMaster CPU核数', 'am_cores', 'intInput', '1', 'yarn模式下AppMaster所需的cpu核心个数', '', '3', '{}',
'3', '1', '1', '1'),
( '901', '90000', '2', 'AppMaster 内存大小', 'am_memory', 'unitInput', '1G', 'yarn模式下AppMaster所需的内存大小，支持单位(G、M、K)', '', '4',
'{"options":[{"value":"G","key":"G"},{"value":"M","key":"M"},{"value":"K","key":"K"}]}','3', '1', '1', '1'),
( '901', '90000', '2', 'Executor 个数', 'worker_num', 'intInput', '1', '所需的Executor个数', '', '5', '{}', '3', '1', '1',
'1'),
( '901', '90000', '2', 'Executor CPU核数', 'worker_cores', 'intInput', '1', '每个Executor服务器cpu核数', '', '6', '{}','3',
'1', '1', '1'),
( '901', '90000', '2', 'Executor 内存大小', 'worker_memory', 'unitInput', '2G', '每个Executor服务器内存大小，支持单位(G、M、K)', '', '7',
'{"options":[{"value":"G","key":"G"},{"value":"M","key":"M"},{"value":"K","key":"K"}]}',  '3', '1', '1', '1');


#添加输出模式选项，不再代码中写死了
#tensorflow
INSERT INTO `bas_node_config` (`node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`, `display`, `required`, `parsing_rule`, `model_op_rule`)
VALUES
    (600, 90005, 1, '输出模式', 'output-mode', 'input', 'overwrite', '输出模式，默认overwrite覆盖，支持append/error/overwrite', '输出模式，默认overwrite覆盖，支持append/error/overwrite', 1, '{}', 2, 1, 1, 1);
#mxnet
INSERT INTO `bas_node_config` (`node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`, `display`, `required`, `parsing_rule`, `model_op_rule`)
VALUES
    (601, 90006, 1, '输出模式', 'output-mode', 'input', 'overwrite', '输出模式，默认overwrite覆盖，支持append/error/overwrite', '输出模式，默认overwrite覆盖，支持append/error/overwrite', 1, '{}', 2, 1, 1, 1);
#caffe2
INSERT INTO `bas_node_config` (`node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`, `display`, `required`, `parsing_rule`, `model_op_rule`)
VALUES
    (602, 90007, 1, '输出模式', 'output-mode', 'input', 'overwrite', '输出模式，默认overwrite覆盖，支持append/error/overwrite', '输出模式，默认overwrite覆盖，支持append/error/overwrite', 1, '{}', 2, 1, 1, 1);
#xgboost
INSERT INTO `bas_node_config` (`node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`, `display`, `required`, `parsing_rule`, `model_op_rule`)
VALUES
    (603, 90008, 1, '输出模式', 'output-mode', 'input', 'overwrite', '输出模式，默认overwrite覆盖，支持append/error/overwrite', '输出模式，默认overwrite覆盖，支持append/error/overwrite', 1, '{}', 2, 1, 1, 1);
#lightgbm
INSERT INTO `bas_node_config` (`node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`, `display`, `required`, `parsing_rule`, `model_op_rule`)
VALUES
    (604, 90009, 1, '输出模式', 'output-mode', 'input', 'overwrite', '输出模式，默认overwrite覆盖，支持append/error/overwrite', '输出模式，默认overwrite覆盖，支持append/error/overwrite', 1, '{}', 2, 1, 1, 1);

#输入输出端点表和dag连线表新增字段
#数据迁移
UPDATE bas_instance_input_port a , rel_instance_config_value b SET a.config_id = b.id WHERE a.`code` = b.`code` AND a.instance_id = b.instance_id;
#数据迁移
UPDATE bas_instance_output_port a , rel_instance_config_value b SET a.config_id = b.id WHERE a.`code` = b.`code` AND a.instance_id = b.instance_id;
#数据迁移
UPDATE rel_instance_graph a , rel_instance_config_value b SET a.input_config_id = b.id WHERE a.source = b.instance_id AND a.input_code = b.`code`;
UPDATE rel_instance_graph a , rel_instance_config_value b SET a.output_config_id = b.id WHERE a.target = b.instance_id AND a.output_code = b.`code`;

#401 皮尔森系数
DELETE FROM bas_node WHERE node_id = '401';
DELETE FROM bas_tab WHERE tab_id = '401';
DELETE FROM rel_node_port WHERE node_id = '401';
DELETE FROM bas_node_config WHERE node_id = '401';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('401', '4', '皮尔森系数', 'pearson', '', '4', '2', '4', '1');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('401','皮尔森系数');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('401', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('401', '评估结果表', '1', '评估结果表', 'output', 'output_table', '1');
#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('401','401','1','输入表','input_table','input','','','','1','{}','2','1',1),
('401','401','1','评估结果表','output_table','input','','','','2','{}','2','1',1),
('401','401','1','第一列名称','col1_name','selectField','','','','3','{"limitSize":1}','3','1',4),
('401','401','1','第二列名称','col2_name','selectField','','','','4','{"limitSize":1}','3','1',4);
#402 T检验
DELETE FROM bas_node WHERE node_id = '402';
DELETE FROM bas_tab WHERE tab_id = '402';
DELETE FROM rel_node_port WHERE node_id = '402';
DELETE FROM bas_node_config WHERE node_id = '402';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('402', '4', 'T检验', 'ttest', '', '4', '2', '4', '2');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('402','T检验');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('402', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('402', '评估结果表', '1', '评估结果表', 'output', 'output_table', '1');
#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('402','402','1','输入表','input_table','input','','','','1','{}','2','1',1),
('402','402','1','评估结果表','output_table','input','','','','2','{}','2','1',1),
('402','402','1','需要进行t检验的列','col_name','selectField','','','','3','{"limitSize":1}','3','1',4),
('402','402','1','对立假设','alternative','select','two.sided','','','4','{"options":[{"key":"two.sided","value":"two.sided"},{"key":"less","value":"less"},{"key":"greater","value":"greater"}]}','3','0',1),
('402','402','1','置信度','confidence_level','select','0.95','','','5','{"options":[{"key":"0.8","value":"0.8"},{"key":"0.9","value":"0.9"},{"key":"0.95","value":"0.95"},{"key":"0.99","value":"0.99"},{"key":"0.995","value":"0.995"},{"key":"0.999","value":"0.999"}]}','3','0',1),
('402','402','1','假设均值','mu','floatInput','0.0','','','6','{}','3','0',1);
#403 离散值特征分析
DELETE FROM bas_node WHERE node_id = '403';
DELETE FROM bas_tab WHERE tab_id = '403';
DELETE FROM rel_node_port WHERE node_id = '403';
DELETE FROM bas_node_config WHERE node_id = '403';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('403', '4', '离散值特征分析', 'enumfeatureselection', '', '4', '2', '4', '3');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('403','离散值特征分析');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('403', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('403', '离散特征的枚举值分布表    ', '1', '离散特征的枚举值分布表    ', 'output', 'output_cnt_table', '1'),
('403', '离散特征的gini、entropy输出表', '2', '离散特征的gini、entropy输出表', 'output', 'output_value_table', '1'),
('403', '离散特征枚举值gini、entropy输出表', '3', '离散特征枚举值gini、entropy输出表', 'output', 'output_enum_value_table', '1');
#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('403','403','1','输入表','input_table','input','','','','1','{}','2','1',1),
('403','403','1','离散特征的枚举值分布表','output_cnt_table','input','','','','2','{}','2','1',1),
('403','403','1','离散特征的gini、entropy输出表','output_value_table','input','','','','3','{}','2','1',1),
('403','403','1','离散特征枚举值gini、entropy输出表','output_enum_value_table','input','','','','4','{}','2','1',1),
('403','403','1','标签列','label_col','selectField','','','','5','{"limitSize":1}','3','1',4),
('403','403','1','特征列','feature_cols','selectField','','','','6','{}','3','0',4);
#405 百分位
DELETE FROM bas_node WHERE node_id = '405';
DELETE FROM bas_tab WHERE tab_id = '405';
DELETE FROM rel_node_port WHERE node_id = '405';
DELETE FROM bas_node_config WHERE node_id = '405';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('405', '4', '百分位', 'percentile', '', '4', '2', '4', '5');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('405','百分位');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('405', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('405', '输出表', '1', '输出表', 'output', 'output_table', '1');
#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('405','405','1','输入表','input_table','input','','','','1','{}','2','1',1),
('405','405','1','输出表','output_table','input','','','','2','{}','2','1',1),
('405','405','1','需要统计百分位的列','selected_col','selectField','','','','3','{"limitSize":1}','3','1',4),
('405','405','1','相对错误率','relative_error','floatInput','0','','','4','{"type":"float", "max":1, "min":0}','3','0',1);
#406 全表统计
DELETE FROM bas_node WHERE node_id = '406';
DELETE FROM bas_tab WHERE tab_id = '406';
DELETE FROM rel_node_port WHERE node_id = '406';
DELETE FROM bas_node_config WHERE node_id = '406';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('406', '4', '全表统计', 'statsummary', '', '4', '2', '4', '6');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('406','全表统计');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('406', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('406', '输出表', '1', '输出表', 'output', 'output_table', '1');
#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('406','406','1','输入表','input_table','input','','','','1','{}','2','1',1),
('406','406','1','输出表','output_table','input','','','','2','{}','2','1',1),
('406','406','1','需要进行基本统计列的列','selected_cols','selectField','','','','3','{}','3','1',4);
#407 直方图
DELETE FROM bas_node WHERE node_id = '407';
DELETE FROM bas_tab WHERE tab_id = '407';
DELETE FROM rel_node_port WHERE node_id = '407';
DELETE FROM bas_node_config WHERE node_id = '407';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('407', '4', '直方图', 'histogram', '', '4', '2', '4', '7');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('407','直方图');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('407', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('407', '输出表', '1', '输出表', 'output', 'output_table', '1');
#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('407','407','1','输入表','input_table','input','','','','1','{}','2','1',1),
('407','407','1','输出表','output_table','input','','','','2','{}','2','1',1),
('407','407','1','需要统计直方图信息列的列','selected_cols','selectField','','','','3','{}','3','1',4),
('407','407','1','分箱数','bins','intInput','','','','4','{}','3','1',1);
#408 协方差
DELETE FROM bas_node WHERE node_id = '408';
DELETE FROM bas_tab WHERE tab_id = '408';
DELETE FROM rel_node_port WHERE node_id = '408';
DELETE FROM bas_node_config WHERE node_id = '408';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('408', '4', '协方差', 'cov', '', '4', '2', '4', '8');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('408','协方差');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('408', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('408', '输出表', '1', '输出表', 'output', 'output_table', '1');
#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('408','408','1','输入表','input_table','input','','','','1','{}','2','1',1),
('408','408','1','输出表','output_table','input','','','','2','{}','2','1',1),
('408','408','1','需要统计协方差列的列','selected_cols','selectField','','','','3','{}','3','1',4);
#409 相关系数矩阵
DELETE FROM bas_node WHERE node_id = '409';
DELETE FROM bas_tab WHERE tab_id = '409';
DELETE FROM rel_node_port WHERE node_id = '409';
DELETE FROM bas_node_config WHERE node_id = '409';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('409', '4', '相关系数矩阵', 'corrcoef', '', '4', '2', '4', '9');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('409','相关系数矩阵');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('409', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('409', '输出表', '1', '输出表', 'output', 'output_table', '1');
#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('409','409','1','输入表','input_table','input','','','','1','{}','2','1',1),
('409','409','1','输出表','output_table','input','','','','2','{}','2','1',1),
('409','409','1','需要计算相关性的列','selected_cols','selectField','','','','3','{}','3','1',4);
#410 正态检验
DELETE FROM bas_node WHERE node_id = '410';
DELETE FROM bas_tab WHERE tab_id = '410';
DELETE FROM rel_node_port WHERE node_id = '410';
DELETE FROM bas_node_config WHERE node_id = '410';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('410', '4', '正态检验', 'normalitytest', '', '4', '2', '4', '10');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('410','正态检验');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('410', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('410', '输出表', '1', '输出表', 'output', 'output_table', '1');
#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('410','410','1','输入表','input_table','input','','','','1','{}','2','1',1),
('410','410','1','输出表','output_table','input','','','','2','{}','2','1',1),
('410','410','1','需要统计相关系数的列','selected_cols','selectField','','','','3','{}','3','1',4),
('410','410','1','是否绘制qq图','enable_QQplot','boolean','true','','','4','{}','3','0',1),
('410','410','1','是否进行Anderson-Darling检验','enable_ADtest','boolean','true','','','5','{}','3','0',1),
('410','410','1','是否进行Kolmogorov-Smirnov检验','enable_KStest','boolean','true','','','6','{}','3','0',1);
#411 洛伦兹曲线
DELETE FROM bas_node WHERE node_id = '411';
DELETE FROM bas_tab WHERE tab_id = '411';
DELETE FROM rel_node_port WHERE node_id = '411';
DELETE FROM bas_node_config WHERE node_id = '411';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('411', '4', '洛伦兹曲线', 'lorenzcurve', '', '4', '2', '4', '11');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('411','洛伦兹曲线');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('411', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('411', '输出表', '1', '输出表', 'output', 'output_table', '1');
#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('411','411','1','输入表','input_table','input','','','','1','{}','2','1',1),
('411','411','1','输出表','output_table','input','','','','2','{}','2','1',1),
('411','411','1','需要统计洛伦兹曲线信息列的列','selected_cols','selectField','','','','3','{}','3','1',4),
('411','411','1','分位数','n','intInput','100','','','4','{}','3','0',1);
#412 经验概率密度图
DELETE FROM bas_node WHERE node_id = '412';
DELETE FROM bas_tab WHERE tab_id = '412';
DELETE FROM rel_node_port WHERE node_id = '412';
DELETE FROM bas_node_config WHERE node_id = '412';
#插入新组件
INSERT INTO `bas_node`
(`node_id`, `type`, `name`, `code`, `descr`, `node_catalog_id`, `status`, `icon`, `index`) values
('412', '4', '经验概率密度图', 'empirical_pdf', '', '4', '2', '4', '12');
#组件tab
INSERT INTO `bas_tab` (`tab_id`, `tab_name`) values('412','经验概率密度图');
#组件端口
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('412', '输入表', '1', '输入表', 'input', 'input_table', '1'),
('412', '输出表', '1', '输出表', 'output', 'output_table', '1');

#组件配置项
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('412','412','1','输入表','input_table','input','','','','1','{}','2','1',1),
('412','412','1','输出表','output_table','input','','','','2','{}','2','1',1),
('412','412','1','输入列','feature_col','selectField','','','','3','{}','3','1',4),
('412','412','1','标签列','label_col','selectField','','','','4','{"limitSize":1}','3','0',4),
('412','412','1','计算频次区间数','interval_num','intInput','50','','','5','{}','3','0',1);


DELETE FROM rel_instance_config_value WHERE `code` = 'stopwords_col';
DELETE FROM bas_instance_input_port WHERE `code`='stopwords_table';

#zf组件配置修改1210
#PLDA 添加remain_col(保留列)
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('703','703','1','保留列','remain_col','selectField','','','','1','{}','3','0',4);
#文章摘要 添加stopwords_col(停用词列),改成ID列,文档内容列并设置单选
update bas_node_config set param='{"limitSize":1}' where (node_id=709 and `code`='doc_id_col');
update bas_node_config set `name`='文档内容列', param='{"limitSize":1}' where (node_id=709 and `code`='txt_col');
#关键词提取    添加stopwords_col(停用词列),标识文章id的列名,word列设置为单选
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('711','711','1','停用词列','stopwords_col','selectField','','','','1','{"limitSize":1}','3','1',4);
update bas_node_config set  param='{"limitSize":1}' where (node_id=711 and `code`='doc_id_col');
update bas_node_config set  param='{"limitSize":1}' where (node_id=711 and `code`='doc_content_col');
#去停用词 添加stopwords_col(停用词列)
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('6','6','1','停用词列','stopwords_col','selectField','','','','1','{"limitSize":1}','3','1',4);
#word2vec 单词列该成文档列,并设置单选
update bas_node_config set  `name`='文档列', param='{"limitSize":1}' where (node_id=701 and `code`='sent_col_name');
#TF-IDF   全部设置单选
update bas_node_config set param='{"limitSize":1}' where (node_id=702 and `code`='doc_id');
update bas_node_config set param='{"limitSize":1}' where (node_id=702 and `code`='doc_word');
update bas_node_config set param='{"limitSize":1}' where (node_id=702 and `code`='doc_count');
#文章相似度    相似度计算第一二列设置单选
update bas_node_config set param='{"limitSize":1}' where (node_id=704 and `code`='inputSelectedColName1');
update bas_node_config set param='{"limitSize":1}' where (node_id=704 and `code`='inputSelectedColName2');
#PMI  改成文档列并设置单选 截断的最小词频设置默认值5
update bas_node_config set param='{"limitSize":1}' where (node_id=705 and `code`='doc_col');
update bas_node_config set `value`=5 where (node_id=705 and `code`='min_count');
#Doc2Vec  改成ID列 文档内容列并设置单选
update bas_node_config set `name`='ID列', param='{"limitSize":1}' where (node_id=706 and `code`='doc_id_name');
update bas_node_config set `name`='文档内容列', param='{"limitSize":1}' where (node_id=706 and `code`='doc_col_name');
#字符串相似度   相似度计算第一二列设置单选,输出表追加的列名跟第二列调换位置
update bas_node_config set param='{"limitSize":1}' where (node_id=713 and `code`='input_selected_col1');
update bas_node_config set `index`=1, param='{"limitSize":1}' where (node_id=713 and `code`='input_selected_col2');
#句子拆分 改成ID列 文档内容列并设置单选
update bas_node_config set param='{"limitSize":1}' where (node_id=714 and `code`='doc_id_col');
update bas_node_config set `name`='文档内容列', param='{"limitSize":1}' where (node_id=714 and `code`='doc_content');
#三元组转换    所以需要选择列的配置都设置为单选
update bas_node_config set `name`='转成kv表时保持不变的列名', param='{"limitSize":1}' where (node_id=715 and `code`='id_col');
update bas_node_config set `name`='kv中的key', param='{"limitSize":1}' where (node_id=715 and `code`='key_col');
update bas_node_config set `name`='kv中的value', param='{"limitSize":1}' where (node_id=715 and `code`='value_col');
update bas_node_config set `name`='输入索引表key的列', param='{"limitSize":1}' where (node_id=715 and `code`='index_input_key_col');
update bas_node_config set `name`='输入索引表key索引号的列', param='{"limitSize":1}' where (node_id=715 and `code`='index_input_key_id_col');
#朴素贝叶斯    标签列 权重列设置单选
update bas_node_config set  param='{"limitSize":1}' where (node_id=502 and `code`='label_col');
update bas_node_config set  param='{"limitSize":1}' where (node_id=502 and `code`='weight_col');
#线性回归 标签列 权重列设置单选
update bas_node_config set  param='{"limitSize":1}' where (node_id=506 and `code`='label_col');
update bas_node_config set  param='{"limitSize":1}' where (node_id=506 and `code`='weight_col');
#决策树分类    标签列设置单选
update bas_node_config set  param='{"limitSize":1}' where (node_id=803 and `code`='label_col');
#GBDT回归   标签列设置单选
update bas_node_config set  param='{"limitSize":1}' where (node_id=805 and `code`='label_col');
#文章摘要
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('709','709','1','停用词列','stopwords_col','selectField','','','','4','{"limitSize":1}','3','1',4);
update bas_node_config set  type='input', display='2' where (node_id=709 and code='stopwords_table');

#更改组件配置项顺序
update bas_node_config set `index`=0 where (node_id=711 and `code`='stopwords_col');
update bas_node_config set `index`=3 where (node_id=6 and `code`='stopwords_col');
#文本摘要端口修改
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('709', '输入停用词表', '2', '输入停用词表', 'input', 'stopwords_table', '1');
update bas_node_config set type='input', display=2 where(node_id=709 and `code`='stopwords_table');
#关键词抽取端口修改
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('711', '输入停用词表', '2', '输入停用词表', 'input', 'stopwords_table', '1');
update bas_node_config set type='input', display=2 where(node_id=711 and `code`='stopwords_table');
#去停用词端口修改
INSERT INTO `rel_node_port`
( `node_id`, `name`, `index`, `descr`, `type`, `code`, `io_type`) values
('6', '输入停用词表', '2', '输入停用词表', 'input', 'stopwords_table', '1');
update bas_node_config set type='input', display=2 where(node_id=6 and `code`='stopwords_table');
update bas_node_config set `name`='选择去停用词列' where(node_id=6 and `code`='selected_cols');
#三元组转kv link表配置
INSERT INTO `rel_node_config_link`
( `node_id`, `node_code`, `src_code`, `dest_code`, `src_value`, `action`, `invalid`, `required`) values
('715', 'triple2kv', 'id_col', 'input_table', '', 'input', '0', '1'),
('715', 'triple2kv', 'key_col', 'input_table', '', 'input', '0', '1'),
('715', 'triple2kv', 'value_col', 'input_table', '', 'input', '0', '1'),
('715', 'triple2kv', 'index_input_key_col', 'index_input_table', '', 'input', '0', '1'),
('715', 'triple2kv', 'index_input_key_id_col', 'index_input_table', '', 'input', '0', '1');
#文本摘要 link表配置
INSERT INTO `rel_node_config_link`
( `node_id`, `node_code`, `src_code`, `dest_code`, `src_value`, `action`, `invalid`, `required`) values
('709', 'abstract_generate', 'doc_id_col', 'input_table', '', 'input', '0', '1'),
('709', 'abstract_generate', 'txt_col', 'input_table', '', 'input', '0', '1'),
('709', 'abstract_generate', 'stopwords_col', 'stopwords_table', '', 'input', '0', '1');
#关键词抽取 link表配置
INSERT INTO `rel_node_config_link`
( `node_id`, `node_code`, `src_code`, `dest_code`, `src_value`, `action`, `invalid`, `required`) values
('711', 'keywords_extraction', 'doc_id_col', 'input_table', '', 'input', '0', '1'),
('711', 'keywords_extraction', 'doc_content_col', 'input_table', '', 'input', '0', '1'),
('711', 'keywords_extraction', 'stopwords_col', 'stopwords_table', '', 'input', '0', '1');
#去停用词 link表配置
INSERT INTO `rel_node_config_link`
( `node_id`, `node_code`, `src_code`, `dest_code`, `src_value`, `action`, `invalid`, `required`) values
('6', 'stop_words_filter', 'selected_cols', 'input_table', '', 'input', '0', '1'),
('6', 'stop_words_filter', 'remain_cols', 'input_table', '', 'input', '0', '1'),
('6', 'stop_words_filter', 'stopwords_col', 'stopwords_table', '', 'input', '0', '1');
#二分类评估
update bas_node set invalid=0 where node_id=515;
#------------------#------------------#------------------#------------------#------------------#------------------#------------------#--------------------
#zf文本组件配置修改1211
update bas_node_config set `name`='待去停用词列' where (node_id=6 and `code`='selected_cols');
update bas_node_config set `name`='待索引化列' where (node_id=7 and `code`='selected_cols');
update bas_node_config set `name`='文档ID列' where (node_id=700 and `code`='doc_id');
update bas_node_config set `name`='文档内容列' where (node_id=700 and `code`='doc_content');
update bas_node_config set `name`='文档内容列' where (node_id=701 and `code`='sent_col_name');
update bas_node_config set `name`='学习率' where (node_id=701 and `code`='alpha');
update bas_node_config set `name`='迭代次数' where (node_id=701 and `code`='iter_train');
update bas_node_config set `name`='句子最大长度' where (node_id=701 and `code`='max_sentence_length');
update bas_node_config set `required`=0 where (node_id=701 and `code`='layer_size');
update bas_node_config set `required`=0 where (node_id=701 and `code`='min_count');
update bas_node_config set `required`=0 where (node_id=701 and `code`='num_partitions');
update bas_node_config set `required`=0 where (node_id=701 and `code`='alpha');
update bas_node_config set `required`=0 where (node_id=701 and `code`='iter_train');
update bas_node_config set `required`=0 where (node_id=701 and `code`='seed');
update bas_node_config set `required`=0 where (node_id=701 and `code`='window');
update bas_node_config set `required`=0 where (node_id=701 and `code`='max_sentence_length');
update bas_node_config set `name`='ID列' where (node_id=702 and `code`='doc_id');
update bas_node_config set `name`='单词列' where (node_id=702 and `code`='doc_word');
update bas_node_config set `name`='单词数量列' where (node_id=702 and `code`='doc_count');
#plda 位置修改
update bas_node_config set `name`='特征列',`index`=1,tip='输入表中需要PLDA分析的列，仅支持kv格式的输入，一般为三元组转kv结果' where (node_id=703 and `code`='selected_cols');
update bas_node_config set `index`=2 where (node_id=703 and `code`='remain_col');
update bas_node_config set `name`='主题个数',`index`=3 where (node_id=703 and `code`='topic_num');
update bas_node_config set `name`='alpha参数',`index`=4,tip='P(z/d)的先验狄利克雷分布的参数，必须>1.0' where (node_id=703 and `code`='alpha');
update bas_node_config set `name`='beta参数',`index`=5,tip='P(w/z)的先验狄利克雷分布的参数，必须>1.0' where (node_id=703 and `code`='beta');
update bas_node_config set `index`=6 where (node_id=703 and `code`='max_terms_per_topic');
update bas_node_config set `name`='迭代次数',`index`=7 where (node_id=703 and `code`='total_iter');
update bas_node_config set `name`='项分隔符',`index`=8 where (node_id=703 and `code`='item_delimiter');
update bas_node_config set `name`='键值对分隔符',`index`=9 where (node_id=703 and `code`='kv_delimiter');
update bas_node_config set `name`='相似度计算的第一列' where (node_id=704 and `code`='inputSelectedColName1');
update bas_node_config set `name`='相似度计算的第二列' where (node_id=704 and `code`='inputSelectedColName2');
update bas_node_config set `name`='相似度列的名称',required=0 where (node_id=704 and `code`='outputColName');
update bas_node_config set `name`='保留列',required=0 where (node_id=704 and `code`='inputAppendColNames');
update bas_node_config set required=0 where (node_id=704 and `code`='method');
update bas_node_config set `name`='文档内容列',tip='文档内容列，一般为分词后的结果，单词间以空格分隔' where (node_id=705 and `code`='doc_col');
update bas_node_config set required=0 where (node_id=705 and `code`='window_size');
update bas_node_config set required=0 where (node_id=705 and `code`='min_count');
update bas_node_config set param='{"options":[{"key":"DM模型","value":1},{"key":"DBOW模型","value":0}]}',`value`='DM模型' where (node_id=706 and `code`='c_bow');
update bas_node_config set `name`='负采样',tip='如果大于0，则指定给定数目的噪声词汇，通常取值5~20，如果为0，则不进行负采样' where (node_id=706 and `code`='negative');
update bas_node_config set `name`='学习率',`display`=2 where (node_id=706 and `code`='alpha');
update bas_node_config set `name`='迭代次数' where (node_id=706 and `code`='iter_train');
update bas_node_config set `display`=2 where (node_id=706 and `code`='random_window');
update bas_node_config set `display`=2 where (node_id=706 and `code`='hs');
update bas_node_config set `required`=0 where (node_id=706 and `code`='layer_size');
update bas_node_config set `required`=0 where (node_id=706 and `code`='c_bow');
update bas_node_config set `required`=0 where (node_id=706 and `code`='window');
update bas_node_config set `required`=0 where (node_id=706 and `code`='min_count');
update bas_node_config set `required`=0 where (node_id=706 and `code`='hs');
update bas_node_config set `required`=0 where (node_id=706 and `code`='sample');
update bas_node_config set `required`=0 where (node_id=706 and `code`='alpha');
update bas_node_config set `required`=0 where (node_id=706 and `code`='iter_train');
update bas_node_config set `required`=0 where (node_id=706 and `code`='random_window');
update bas_node_config set `required`=0 where (node_id=706 and `code`='negative');
update bas_node_config set `param`='{"type":"int","min":0,"max":1e-5}' where (node_id=706 and `code`='sample');
update bas_node_config set `name`='相似度计算的第一列',required=1 where (node_id=713 and `code`='input_selected_col1');
update bas_node_config set `name`='相似度计算的第二列',required=1 where (node_id=713 and `code`='input_selected_col2');
update bas_node_config set `name`='保留列' where (node_id=713 and `code`='input_append_cols');
update bas_node_config set `name`='相似度列的名称' where (node_id=713 and `code`='output_col');
update bas_node_config set `tip`='' where (node_id=713 and `code`='input_table');
update bas_node_config set `tip`='' where (node_id=713 and `code`='output_table');
update bas_node_config set `tip`='' where (node_id=713 and `code`='input_selected_col1');
update bas_node_config set `tip`='' where (node_id=713 and `code`='input_selected_col2');
update bas_node_config set `tip`='' where (node_id=713 and `code`='input_append_cols');
update bas_node_config set `tip`='' where (node_id=713 and `code`='input_partitions');
update bas_node_config set `tip`='' where (node_id=713 and `code`='output_col');
update bas_node_config set `tip`='' where (node_id=713 and `code`='method');
update bas_node_config set `tip`='匹配字符串的权重，仅在ssk计算方法中可用' where (node_id=713 and `code`='lambdaqz');
update bas_node_config set `tip`='子串的长度，在ssk和cosine计算方法中可用' where (node_id=713 and `code`='k');
update bas_node_config set `name`='文档内容列' where (node_id=707 and `code`='input_col_name');
update bas_node_config set `name`='文档ID列' where (node_id=714 and `code`='doc_id_col');
update bas_node_config set `name`='输出ID的列名' where (node_id=714 and `code`='output_doc_id_name');
update bas_node_config set `tip`='句子的间隔字符集合，多个字符以"|"间隔' where (node_id=714 and `code`='delimiter');
update bas_node_config set `tip`='' where (node_id=714 and `code`='input_table_name');
update bas_node_config set `tip`='' where (node_id=714 and `code`='output_table_name');
update bas_node_config set `tip`='' where (node_id=714 and `code`='doc_id_col');
update bas_node_config set `tip`='' where (node_id=714 and `code`='doc_content');
update bas_node_config set `tip`='' where (node_id=714 and `code`='output_doc_id_name');
update bas_node_config set `tip`='' where (node_id=714 and `code`='output_sentence_name');
update bas_node_config set `name`='分词方法',tip='分词方法， 目前仅支持jieba' where (node_id=708 and `code`='which_method');
update bas_node_config set `name`='是否包含词性标注' where (node_id=708 and `code`='part_of_speech');
update bas_node_config set `name`='待分词列' where (node_id=708 and `code`='selected_cols');
update bas_node_config set `name`='保留列', tip='' where (node_id=708 and `code`='remain_cols');
#分词处理  待分词列-最上面
update bas_node_config set `name`='分词方法',tip='分词方法， 目前仅支持jieba',required=0 where (node_id=708 and `code`='which_method');
update bas_node_config set `name`='是否包含词性标注' where (node_id=708 and `code`='part_of_speech');
update bas_node_config set `name`='待分词列',`index`=0 where (node_id=708 and `code`='selected_cols');
update bas_node_config set `name`='保留列', tip='', required=0 where (node_id=708 and `code`='remain_cols');
#文本摘要 停用词列移到文档内容列下
update bas_node_config set `name`='文档ID列' where (node_id=709 and `code`='doc_id_col');
update bas_node_config set `index`=6 where (node_id=709 and `code`='stopwords_col');
update bas_node_config set `name`='最大迭代次数' where (node_id=709 and `code`='max_iter');
update bas_node_config set required=0 where (node_id=709 and `code`='top_n');
update bas_node_config set required=0 where (node_id=709 and `code`='split_str');
update bas_node_config set required=0 where (node_id=709 and `code`='damping_factor');
update bas_node_config set required=0 where (node_id=709 and `code`='max_iter');
update bas_node_config set required=0 where (node_id=709 and `code`='epsilon');
update bas_node_config set required=0 where (node_id=709 and `code`='method');
update bas_node_config set `name`='ID列' where (node_id=710 and `code`='id_col');
update bas_node_config set `name`='向量列' where (node_id=710 and `code`='vector_cols');
update bas_node_config set `name`='距离最近的向量个数' where (node_id=710 and `code`='top_n');
#关键词抽取 停用词列移到文档内容列下
update bas_node_config set `name`='文档ID列' where (node_id=711 and `code`='doc_id_col');
update bas_node_config set `name`='文档内容列' where (node_id=711 and `code`='doc_content_col');
update bas_node_config set `name`='输出的关键词数量' where (node_id=711 and `code`='top_n');
update bas_node_config set `name`='窗口大小' where (node_id=711 and `code`='window_size');
update bas_node_config set `name`='阻尼系数' where (node_id=711 and `code`='dumping_factor');
update bas_node_config set `name`='最大迭代次数' where (node_id=711 and `code`='max_iter');
update bas_node_config set `name`='收敛残差阈值' where (node_id=711 and `code`='epsilon');
#------------------#------------------#------------------#------------------#------------------#------------------#------------------#--------------------

#添加tab项
DELETE FROM bas_tab WHERE tab_id = 90003;
INSERT INTO bas_tab(tab_id, tab_name, ctime, mtime, invalid) VALUES (90003, '资源配置', SYSDATE(), SYSDATE(), 0);
DELETE FROM bas_tab WHERE tab_id = 90004;
INSERT INTO bas_tab(tab_id, tab_name, ctime, mtime, invalid) VALUES (90004, '资源配置', SYSDATE(), SYSDATE(), 0);
DELETE FROM bas_tab WHERE tab_id = 90005;
INSERT INTO bas_tab(tab_id, tab_name, ctime, mtime, invalid) VALUES (90005, '参数配置', SYSDATE(), SYSDATE(), 0);
DELETE FROM bas_tab WHERE tab_id = 90006;
INSERT INTO bas_tab(tab_id, tab_name, ctime, mtime, invalid) VALUES (90006, '参数配置', SYSDATE(), SYSDATE(), 0);
DELETE FROM bas_tab WHERE tab_id = 90007;
INSERT INTO bas_tab(tab_id, tab_name, ctime, mtime, invalid) VALUES (90007, '参数配置', SYSDATE(), SYSDATE(), 0);
DELETE FROM bas_tab WHERE tab_id = 90008;
INSERT INTO bas_tab(tab_id, tab_name, ctime, mtime, invalid) VALUES (90008, '参数配置', SYSDATE(), SYSDATE(), 0);
DELETE FROM bas_tab WHERE tab_id = 90009;
INSERT INTO bas_tab(tab_id, tab_name, ctime, mtime, invalid) VALUES (90009, '参数配置', SYSDATE(), SYSDATE(), 0);
DELETE FROM bas_tab WHERE tab_id = 90010;
INSERT INTO bas_tab(tab_id, tab_name, ctime, mtime, invalid) VALUES (90010, '参数配置', SYSDATE(), SYSDATE(), 0);


#修改config项对应的tab项
UPDATE bas_node_config SET tab_id = 90005 WHERE code = "__launchVenv" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90005 WHERE code = "__codeFiles" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90005 WHERE code = "__launchFile" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90005 WHERE code = "__launchArgs" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90005 WHERE code = "app-type" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90005 WHERE code = "input_path" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90005 WHERE code = "output_path" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90006 WHERE code = "launch_venv" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90006 WHERE code = "__codeFiles" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90006 WHERE code = "__launchFile" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90006 WHERE code = "__launchArgs" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90006 WHERE code = "app-type" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90006 WHERE code = "input_path" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90006 WHERE code = "output_path" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90007 WHERE code = "launch_venv" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90007 WHERE code = "__codeFiles" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90007 WHERE code = "__launchFile" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90007 WHERE code = "__launchArgs" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90007 WHERE code = "app-type" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90007 WHERE code = "input_path" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90007 WHERE code = "output_path" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90008 WHERE code = "launch_venv" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90008 WHERE code = "__codeFiles" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90008 WHERE code = "__launchFile" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90008 WHERE code = "__launchArgs" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90008 WHERE code = "app-type" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90008 WHERE code = "input_path" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90008 WHERE code = "model_path" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90009 WHERE code = "launch_venv" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90009 WHERE code = "__codeFiles" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90009 WHERE code = "__launchFile" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90009 WHERE code = "__launchArgs" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90009 WHERE code = "app-type" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90009 WHERE code = "input_path" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90009 WHERE code = "output_path" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_num" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_cores" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_memory" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_num" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_cores" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_gcores" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_memory" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90010 WHERE code = "__codeFiles" AND node_id=605;
UPDATE bas_node_config SET tab_id = 90010 WHERE code = "__launchFile" AND node_id=605;
UPDATE bas_node_config SET tab_id = 90010 WHERE code = "__launchArgs" AND node_id=605;
UPDATE bas_node_config SET tab_id = 90010 WHERE code = "input_path" AND node_id=605;
UPDATE bas_node_config SET tab_id = 90010 WHERE code = "model_path" AND node_id=605;
UPDATE bas_node_config SET tab_id = 90010 WHERE code = "output_path" AND node_id=605;
UPDATE bas_node_config SET tab_id = 90003 WHERE code = "worker_num" AND node_id=605;
UPDATE bas_node_config SET tab_id = 90003 WHERE code = "worker_cores" AND node_id=605;
UPDATE bas_node_config SET tab_id = 90003 WHERE code = "worker_gcores" AND node_id=605;
UPDATE bas_node_config SET tab_id = 90003 WHERE code = "worker_memory" AND node_id=605;
UPDATE bas_node_config SET tab_id = 90002 WHERE code = "table" AND node_id=81;
UPDATE bas_node_config SET tab_id = 90002 WHERE code = "table_format" AND node_id=81;
UPDATE bas_node_config SET tab_id = 90002 WHERE code = "file_type" AND node_id=81;
UPDATE bas_node_config SET tab_id = 90002 WHERE code = "file_path" AND node_id=81;
UPDATE bas_node_config SET tab_id = 90002 WHERE code = "field_def" AND node_id=81;
UPDATE bas_node_config SET tab_id = 90002 WHERE code = "header" AND node_id=81;
UPDATE bas_node_config SET tab_id = 90002 WHERE code = "field_delim" AND node_id=81;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_num" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_cores" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_memory" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_num" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_cores" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_gcores" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_memory" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_num" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_cores" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_memory" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_num" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_cores" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_gcores" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_memory" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_num" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_cores" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_memory" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_num" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_cores" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_gcores" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_memory" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_num" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_cores" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "ps_memory" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_cores" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_gcores" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90004 WHERE code = "worker_memory" AND node_id=604;
UPDATE bas_node_config SET tab_id = 90001 WHERE code = "__engineVersion" AND node_id=901;
UPDATE bas_node_config SET tab_id = 90001 WHERE code = "__launchVenv" AND node_id=901;
UPDATE bas_node_config SET tab_id = 90001 WHERE code = "__codeFiles" AND node_id=901;
UPDATE bas_node_config SET tab_id = 90001 WHERE code = "__launchFile" AND node_id=901;
UPDATE bas_node_config SET tab_id = 90001 WHERE code = "__launchArgs" AND node_id=901;
UPDATE bas_node_config SET tab_id = 90001 WHERE code = "input_port_args" AND node_id=901;
UPDATE bas_node_config SET tab_id = 90001 WHERE code = "output_port_args" AND node_id=901;
UPDATE bas_node_config SET tab_id = 90005 WHERE code = "output-mode" AND node_id=600;
UPDATE bas_node_config SET tab_id = 90006 WHERE code = "output-mode" AND node_id=601;
UPDATE bas_node_config SET tab_id = 90007 WHERE code = "output-mode" AND node_id=602;
UPDATE bas_node_config SET tab_id = 90008 WHERE code = "output-mode" AND node_id=603;
UPDATE bas_node_config SET tab_id = 90009 WHERE code = "output-mode" AND node_id=604;


#机器学习组件目录位置修改
#插入新目录
DELETE FROM bas_node_catalog WHERE `name` = '二分类';
DELETE FROM bas_node_catalog WHERE `name` = '多分类';
DELETE FROM bas_node_catalog WHERE `name` = '聚类';
DELETE FROM bas_node_catalog WHERE `name` = '回归';
insert into `bas_node_catalog` ( `node_catalog_id`, `parent_id`, `name`, `category`, `icon`, `index`) values ( '9', '5', '二分类', 'binaryclassclassification', '5', '1');
insert into `bas_node_catalog` ( `node_catalog_id`, `parent_id`, `name`, `category`, `icon`, `index`) values ( '10', '5', '多分类', 'multiclassclassification', '5', '2');
insert into `bas_node_catalog` ( `node_catalog_id`, `parent_id`, `name`, `category`, `icon`, `index`) values ( '11', '5', '聚类', 'clustering', '5', '3');
insert into `bas_node_catalog` ( `node_catalog_id`, `parent_id`, `name`, `category`, `icon`, `index`) values ( '12', '5', '回归', 'regression', '5', '4');
#二分类
update bas_node set node_catalog_id=9 where `name`='GBDT二分类';
update bas_node set node_catalog_id=9 where `name`='线性支持向量机';
#多分类
update bas_node set node_catalog_id=10 where `name`='决策树分类';
update bas_node set node_catalog_id=10 where `name`='朴素贝叶斯分类';
update bas_node set node_catalog_id=10 where `name`='随机森林分类';
update bas_node set node_catalog_id=10 where `name`='逻辑回归分类';
update bas_node set node_catalog_id=10 where `name`='k近邻';
#聚类
update bas_node set node_catalog_id=11 where `name`='高斯混合聚类';
update bas_node set node_catalog_id=11 where `name`='kmeans';
#回归
update bas_node set node_catalog_id=12 where `name`='GBDT回归';
update bas_node set node_catalog_id=12 where `name`='线性回归';
update bas_node set node_catalog_id=12 where `name`='随机森林回归';

#二分类评估修改
update rel_node_port set invalid=1 where (node_id=515 and `code`='output_detail_table');
update bas_node_config set `name`='预测详细列', tip='预测组件输出的详细结果列' where (node_id=515 and `code`='score_col');


#模型表旧数据迁移
UPDATE bas_bare_model a ,bas_flow b, rel_project_config c  SET a.engine_id = c.engine_id WHERE a.tenant_id = b.tenant_id AND a.flow_id = b.flow_id AND b.project_id = c.project_id;

update bas_node_config set `name`='连续特征列' where (node_id=404 and `code`='continue_cols');
update bas_node_config set `name`='枚举特征列' where (node_id=404 and `code`='category_col');
update bas_node_config set `name`='读数据表' where (node_id=102 and `code`='read_data_table');
update bas_node_config set `name`='写数据表' where (node_id=101 and `code`='write_data_table');

#深度学习主文件不是必填的
update bas_node_config set `tip`='如果上传压缩包填写相对压缩包内部目录结构的相对路径，如果是.py或.pyc文件可不填', required=0 where node_id in (600,601,602,603,604,605) and `code`="__launchFile";
update rel_instance_config_value set required=0 where node_id in (600,601,602,603,604,605) and `code`="__launchFile";

#调整深度学习参数位置
update bas_node_config set `index`=1 where node_id in (600,601,602,603,604,605) and `code`="__launchVenv";
update bas_node_config set `index`=2 where node_id in (600,601,602,603,604,605) and `code`="__codeFiles";
update bas_node_config set `index`=3 where node_id in (600,601,602,603,604,605) and `code`="__launchFile";
update bas_node_config set `index`=5 where node_id in (600,601,602,603,604,605) and `code`="__launchArgs";
update bas_node_config set `index`=1 where node_id in (600,601,602,603,604,605) and `code`="ps_num";
update bas_node_config set `index`=2 where node_id in (600,601,602,603,604,605) and `code`="ps_cores";
update bas_node_config set `index`=3 where node_id in (600,601,602,603,604,605) and `code`="ps_memory";
update bas_node_config set `index`=4 where node_id in (600,601,602,603,604,605) and `code`="worker_num";
update bas_node_config set `index`=5 where node_id in (600,601,602,603,604,605) and `code`="worker_cores";
update bas_node_config set `index`=6 where node_id in (600,601,602,603,604,605) and `code`="worker_gcores";
update bas_node_config set `index`=7 where node_id in (600,601,602,603,604,605) and `code`="worker_memory";


#调整配置项位置
update bas_node_config set required=1 where (node_id=703 and `code`='selected_cols');
update bas_node_config set `index`=0 where (node_id=711 and `code`='doc_id_col');
update bas_node_config set `index`=0 where (node_id=711 and `code`='doc_content_col');


#生成映射表数据
insert into bas_node_tab_link(tab_id,node_id)
select tab_id,node_id from bas_node_config where config_id in
(select config_id from bas_node_config a where config_id in
(select min(config_id) from bas_node_config group by tab_id,node_id));

#ngram_count配置项input_col_name必填
update bas_node_config set required=1 where(node_id=707 and `code`='input_col_name');
update rel_instance_config_value set required=1 where(node_id=707 and `code`='input_col_name');
update bas_node_config set `value`='' where(node_id=709 and `code`='doc_id_col');
update bas_node_config set `value`='' where(node_id=709 and `code`='txt_col');



#修改必填参数校验和排序
update bas_node_config set required=1 where(node_id=511 and `code`='selected_cols');
update bas_node_config set required=1 where(node_id=810 and `code`='selected_cols');
update bas_node_config set required=1 where(node_id=804 and `code`='feature_cols');
update bas_node_config set required=0 where(node_id=800 and `code`='remain_cols');
update bas_node_config set `name`='结果列名' where(node_id=599 and `code`='result_col');
update bas_node_config set required=1 where(node_id=202 and `code`='scale_method');
update bas_node_config set required=1 where(node_id=204 and `code`='soften_method');
update bas_node_config set `name`='需要进行t检验的列' where(node_id=402 and `code`='col_name');
update bas_node_config set `name`='标签列' where(node_id=403 and `code`='label_col');
update bas_node_config set `name`='特征列' where(node_id=403 and `code`='feature_cols');
update bas_node_config set `invalid`=1 where(node_id=404 and `code`='life_cycle');
update bas_node_config set `name`='需要统计百分位的列' where(node_id=405 and `code`='selected_col');
update bas_node_config set `name`='需要进行基本统计的列' where(node_id=406 and `code`='selected_cols');
update bas_node_config set `name`='需要进行直方图信息统计的列' where(node_id=407 and `code`='selected_cols');
update bas_node_config set `name`='需要计算协方差的列' where(node_id=408 and `code`='selected_cols');
update bas_node_config set `name`='需要统计相关系数的列' where(node_id=409 and `code`='selected_cols');
update bas_node_config set `name`='需要正态检验的列' where(node_id=410 and `code`='selected_cols');
update bas_node_config set `name`='是否绘制qq图' where(node_id=410 and `code`='enable_QQplot');
update bas_node_config set `name`='是否进行Anderson-Darling检验' where(node_id=410 and `code`='enable_ADtest');
update bas_node_config set `name`='是否进行Kolmogorov-Smirnov检验' where(node_id=410 and `code`='enable_KStest');
update bas_node_config set `name`='需要统计洛伦兹曲线信息的列' where(node_id=411 and `code`='selected_cols');
update bas_node_config set `name`='标签列' where(node_id=412 and `code`='label_col');
update bas_node_config set `index`=0 where(node_id=701 and `code`='sent_col_name');
update bas_node_config set `index`=0 where(node_id=702 and `code`='doc_id');
update bas_node_config set `index`=0 where(node_id=702 and `code`='doc_word');
update bas_node_config set `index`=0 where(node_id=209 and `code`='selected_cols');
update bas_node_config set `index`=1 where(node_id=209 and `code`='reserve_cols');
update bas_node_config set `index`=2 where(node_id=209 and `code`='append_cols');
update bas_node_config set `index`=3 where(node_id=209 and `code`='output_table_type');
update bas_node_config set `index`=4 where(node_id=209 and `code`='item_delimiter');
update bas_node_config set `index`=5 where(node_id=209 and `code`='drop_last');
update bas_node_config set `index`=6 where(node_id=209 and `code`='kv_delimiter');
update bas_node_config set `index`=0 where(node_id=207 and `code`='selected_cols');
update bas_node_config set `index`=0 where(node_id=704 and `code`='inputSelectedColName1');
update bas_node_config set `index`=1 where(node_id=704 and `code`='inputSelectedColName2');
update bas_node_config set `index`=2 where(node_id=704 and `code`='inputAppendColNames');
update bas_node_config set `index`=3 where(node_id=704 and `code`='method');
update bas_node_config set `index`=4 where(node_id=704 and `code`='outputColName');
update bas_node_config set `index`=0 where(node_id=705 and `code`='doc_col');
update bas_node_config set `index`=0 where(node_id=706 and `code`='doc_id_name');
update bas_node_config set `index`=0 where(node_id=706 and `code`='doc_col_name');
update bas_node_config set `index`=0 where(node_id=714 and `code`='doc_id_col');
update bas_node_config set `index`=0 where(node_id=714 and `code`='doc_content');
update bas_node_config set `index`=0 where(node_id=710 and `code`='id_col');
update bas_node_config set `index`=0 where(node_id=710 and `code`='vector_cols');

-- 添加内存项修改
update bas_node_config set type = 'unitInput', param = '{"options":[{"value":"G","key":"G"},{"value":"M","key":"M"},{"value":"K","key":"K"}]}'
where `name` = 'PS内存' or `name` = 'Worker内存' or `name` = 'Driver 内存' or `name` = 'AppMaster 内存大小' or `name` = 'Executor 内存大小';


-------------------------------------------------v1.2.0--------------------------------------------------------------
--
-------------------------------------------------v1.2.0--------------------------------------------------------------

-- 组件资源配置
DELETE FROM bas_node_tab_link WHERE node_id=1 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,1,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=3 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,3,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=4 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,4,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=6 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,6,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=7 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,7,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=9 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,9,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=10 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,10,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=11 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,11,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=12 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,12,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=13 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,13,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=14 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,14,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=15 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,15,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=16 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,16,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=17 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,17,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=18 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,18,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=19 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,19,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=20 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,20,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=80 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,80,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=81 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,81,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=101 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,101,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=102 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,102,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=103 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,103,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=201 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,201,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=202 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,202,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=203 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,203,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=204 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,204,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=205 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,205,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=206 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,206,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=207 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,207,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=208 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,208,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=209 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,209,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=210 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,210,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=211 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,211,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=401 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,401,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=402 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,402,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=403 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,403,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=404 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,404,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=405 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,405,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=406 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,406,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=407 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,407,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=408 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,408,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=409 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,409,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=410 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,410,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=411 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,411,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=412 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,412,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=413 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,413,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=414 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,414,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=415 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,415,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=416 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,416,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=502 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,502,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=506 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,506,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=508 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,508,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=511 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,511,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=515 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,515,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=599 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,599,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=700 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,700,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=701 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,701,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=702 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,702,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=703 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,703,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=704 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,704,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=705 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,705,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=706 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,706,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=707 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,707,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=708 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,708,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=709 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,709,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=710 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,710,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=711 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,711,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=713 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,713,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=714 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,714,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=715 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,715,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=800 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,800,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=803 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,803,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=804 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,804,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=805 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,805,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=808 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,808,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=809 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,809,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=810 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,810,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=815 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,815,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=816 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,816,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=901 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,901,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=1002 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,1002,90000,CURRENT_TIME,CURRENT_TIME,0);
DELETE FROM bas_node_tab_link WHERE node_id=1003 AND tab_id=90000;
INSERT INTO bas_node_tab_link(id,node_id,tab_id,ctime,mtime,invalid) VALUES(0,1003,90000,CURRENT_TIME,CURRENT_TIME,0);

-- ----------------------------------------------------------------------------------------
update bas_node_config set `code`= 'output_mode' where (node_id=600 and `name`='输出模式');
DELETE FROM rel_node_port WHERE (node_id = 3 and `code`='input_table');
update rel_node_port set invalid=0 where (`code`='output_detail_table' and node_id=515);
DELETE FROM rel_node_port WHERE node_id = '600';
INSERT INTO `bas_node_config`
( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`,`display`, `required`,`parsing_rule`) values
('600','90005','1','输入源','input_port_args','inputPortEdit','','输入源端口设置,输入端口键值对将通过命令行方式传入启动脚本','','8','{"options":[{"value":"1","key":"数据表"}]}','1','0',1),
('600','90005','1','输出源','output_port_args','outputPortEdit','','输出源端口设置,输出端口键值对将通过命令行方式传入启动脚本','','9','{"options":[{"value":"1","key":"数据表"}]}','1','0',1);
update bas_node_config set type='inputPortEdit' where (node_id=3 and `code`='input_table');
update bas_node_config set `code`='input_port_args' where (node_id=3 and `code`='input_table');

update bas_node_config set param='{"nodename":"sql"}' where (node_id=3 and `code`='input_port_args');
update bas_node_config set param='{"options":[{"value":"2","key":"输入文件"}]}' where (node_id=600 and `code`='input_port_args');
update bas_node_config set param='{"options":[{"value":"1","key":"输出表"},{"value":"4","key":"输出模型"}]}' where (node_id=600 and `code`='output_port_args');


-- ----------------------------------------------------------------------------------------

-- tab按照tab_id排序
update bas_tab set tab_id=190000 where tab_id=90000;
update bas_tab set tab_id=190003 where tab_id=90003;
update bas_tab set tab_id=190004 where tab_id=90004;
update bas_node_tab_link set tab_id=190000 where tab_id=90000;
update bas_node_tab_link set tab_id=190003 where tab_id=90003;
update bas_node_tab_link set tab_id=190004 where tab_id=90004;
update bas_node_config set tab_id=190000 where tab_id=90000;
update bas_node_config set tab_id=190003 where tab_id=90003;
update bas_node_config set tab_id=190004 where tab_id=90004;

-- sql修改
update bas_node_config set invalid=1 where (node_id=600 and `code`='input_path');
update bas_node_config set invalid=1 where (node_id=600 and `code`='output_path');


update bas_node_config set param='{"options":[{"key":"DM模型","value":"1"},{"key":"DBOW模型","value":"0"}]}' where (node_id=706 and `code`='c_bow');
update bas_node_config set `value`='1' where (node_id=706 and `code`='c_bow');


update bas_node_config set `code`='output-mode' where `node_id` >= 600 and `node_id` <= 605 and `code`='output_mode';
update bas_node_config set `code`='__launchVenv' where `node_id` >= 600 and `node_id` <= 605 and `code`='launch_venv';
ALTER TABLE rel_project_config ADD instance_id VARCHAR(64) COMMENT '实例id';



-- ---------------------------------v1.2.1-------------------------------------------------------
# 自定义输入输出端口新增文件类型
UPDATE bas_node_config SET param = '{"options":[{"value":"2","key":"输出文件"},{"value":"4","key":"输出模型"}]}' WHERE node_id = 600 AND CODE = 'output_port_args';
UPDATE bas_node_config SET param = '{"options":[{"value":"1","key":"输入表"},{"value":"2","key":"输入文件"}]}' WHERE node_id = 901 AND CODE = 'input_port_args';
UPDATE bas_node_config SET param = '{"options":[{"value":"1","key":"输出表"},{"value":"2","key":"输出文件"}]}' WHERE node_id = 901 AND CODE = 'output_port_args';

# 算法平台修正文件转表组件的资源配置参数,请洋洋执行以下SQL
DELETE FROM bas_tab WHERE tab_id = 90002;
INSERT INTO `bas_tab`(`tab_id`, `tab_name`, `ctime`, `mtime`, `invalid`) VALUES (90002, '参数配置', CURRENT_TIME, CURRENT_TIME, 0);
# 删除写数据表组件端口
update rel_node_port set invalid=1 where node_id=101 and code='output_table';

# 多数据源支持
insert into `bas_node_config` ( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`, `ctime`, `mtime`, `invalid`, `display`, `required`, `parsing_rule`, `model_op_rule`) values ( '101', '101', '1', '目的库', 'output_storage', 'select', '', '必选', '', '0', '{\"options\":[]}', '2019-01-16 11:07:01', '2019-01-16 11:07:01', '0', '2', '0', '1', '1');


insert into `bas_node_config` ( `node_id`, `tab_id`, `tab_index`, `name`, `code`, `type`, `value`, `tip`, `descr`, `index`, `param`, `ctime`, `mtime`, `invalid`, `display`, `required`, `parsing_rule`, `model_op_rule`) values ( '102', '102', '1', '输入数据来源库', 'input_storage', 'select', '', '必选', '', '0', '{\"options\":[]}', '2019-01-15 14:53:43', '2019-01-15 14:53:43', '0', '2', '0', '1', '1');

